(function() {
  var extra = [
      {
          "id": "ai900-206",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A city transit agency is deploying self-service kiosks for riders with diverse accessibility needs.",
              "de": "Eine städtische Verkehrsbehörde führt Selbstbedienungskioske für Fahrgäste mit unterschiedlichen Anforderungen an die Barrierefreiheit ein."
          },
          "question": {
              "en": "Which responsible AI principle is most relevant for designing ticket kiosks that work well with voice input, captions, and screen readers?",
              "de": "Welches Prinzip der verantwortungsvollen KI ist am relevantesten, um Ticketkioske zu entwickeln, die gut mit Spracheingabe, Untertiteln und Screenreadern funktionieren?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Fairness",
                      "de": "Fairness"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Transparency",
                      "de": "Transparenz"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Inclusiveness",
                      "de": "Inklusivität"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Privacy and security",
                      "de": "Datenschutz und Sicherheit"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "Inclusiveness focuses on making AI systems usable by people with different abilities and circumstances.",
              "de": "Inklusivität konzentriert sich darauf, KI-Systeme für Menschen mit unterschiedlichen Fähigkeiten und Umständen nutzbar zu machen."
          },
          "hint": {
              "en": "Look for the principle tied to accessibility and broad participation.",
              "de": "Suchen Sie nach dem Prinzip, das mit Barrierefreiheit und breiter Teilhabe verbunden ist."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2"
      },
      {
          "id": "ai900-207",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which responsible AI principle is emphasized when a chatbot explains why it escalated a refund request to a human agent?",
              "de": "Welches Prinzip der verantwortungsvollen KI wird betont, wenn ein Chatbot erklärt, warum er eine Rückerstattungsanfrage an einen menschlichen Mitarbeiter weitergeleitet hat?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Transparency",
                      "de": "Transparenz"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Fairness",
                      "de": "Fairness"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Reliability and safety",
                      "de": "Zuverlässigkeit und Sicherheit"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Accountability",
                      "de": "Verantwortlichkeit"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Transparency helps users understand how the system behaved and why a decision or action occurred.",
              "de": "Transparenz hilft Benutzern zu verstehen, wie sich das System verhalten hat und warum eine Entscheidung oder Aktion erfolgt ist."
          },
          "hint": {
              "en": "Think about explainability for end users.",
              "de": "Denken Sie an Erklärbarkeit für Endbenutzer."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2"
      },
      {
          "id": "ai900-208",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which responsible AI principle is most directly concerned with protecting files that contain passport numbers and account details?",
              "de": "Welches Prinzip der verantwortungsvollen KI befasst sich am direktesten mit dem Schutz von Dateien, die Passnummern und Kontodaten enthalten?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Privacy and security",
                      "de": "Datenschutz und Sicherheit"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Inclusiveness",
                      "de": "Inklusivität"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Fairness",
                      "de": "Fairness"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Transparency",
                      "de": "Transparenz"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Privacy and security focus on safeguarding sensitive data and controlling how it is accessed and used.",
              "de": "Datenschutz und Sicherheit konzentrieren sich darauf, sensible Daten zu schützen und zu steuern, wie darauf zugegriffen und wie sie verwendet werden."
          },
          "hint": {
              "en": "Choose the principle about protecting confidential information.",
              "de": "Wählen Sie das Prinzip zum Schutz vertraulicher Informationen."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2"
      },
      {
          "id": "ai900-209",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A regional emergency center is evaluating an AI assistant for dispatchers.",
              "de": "Ein regionales Notfallzentrum bewertet einen KI-Assistenten für Disponenten."
          },
          "question": {
              "en": "Which responsible AI principle is most important when an emergency dispatch assistant must operate dependably under stressful real-world conditions?",
              "de": "Welches Prinzip der verantwortungsvollen KI ist am wichtigsten, wenn ein Assistent für die Notrufleitstelle unter belastenden realen Bedingungen zuverlässig arbeiten muss?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Reliability and safety",
                      "de": "Zuverlässigkeit und Sicherheit"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Transparency",
                      "de": "Transparenz"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Privacy and security",
                      "de": "Datenschutz und Sicherheit"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Inclusiveness",
                      "de": "Inklusivität"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Reliability and safety are about consistent behavior, resilience, and minimizing harm in real use.",
              "de": "Zuverlässigkeit und Sicherheit betreffen konsistentes Verhalten, Robustheit und die Minimierung von Schaden im realen Einsatz."
          },
          "hint": {
              "en": "Focus on dependable and safe system behavior.",
              "de": "Konzentrieren Sie sich auf verlässliches und sicheres Systemverhalten."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2"
      },
      {
          "id": "ai900-210",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which machine learning workload predicts a continuous numeric value such as the resale price of a vehicle?",
              "de": "Welcher Machine-Learning-Workload sagt einen kontinuierlichen numerischen Wert wie den Wiederverkaufspreis eines Fahrzeugs voraus?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Regression",
                      "de": "Regression"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Clustering",
                      "de": "Clustering"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Entity recognition",
                      "de": "Entitätserkennung"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Regression is used when the target is a number, such as cost, temperature, or demand.",
              "de": "Regression wird verwendet, wenn das Ziel eine Zahl ist, etwa Kosten, Temperatur oder Nachfrage."
          },
          "hint": {
              "en": "Pick the workload that returns a number rather than a category.",
              "de": "Wählen Sie den Workload, der eine Zahl statt einer Kategorie zurückgibt."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/what-are-ai-services"
      },
      {
          "id": "ai900-211",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which machine learning workload is appropriate for labeling email messages as junk or not junk?",
              "de": "Welcher Machine-Learning-Workload eignet sich dafür, E-Mails als Spam oder Nicht-Spam zu kennzeichnen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Classification",
                      "de": "Klassifizierung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Regression",
                      "de": "Regression"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Clustering",
                      "de": "Clustering"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Translation",
                      "de": "Übersetzung"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Classification assigns items to categories such as junk and not junk.",
              "de": "Die Klassifizierung ordnet Elemente Kategorien wie Spam und Nicht-Spam zu."
          },
          "hint": {
              "en": "Look for the workload that predicts labels or classes.",
              "de": "Suchen Sie nach dem Workload, der Labels oder Klassen vorhersagt."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/what-are-ai-services"
      },
      {
          "id": "ai900-212",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A retail company wants to segment customers but has not defined customer segments in advance.",
              "de": "Ein Einzelhandelsunternehmen möchte Kunden segmentieren, hat jedoch noch keine Kundensegmente vorab definiert."
          },
          "question": {
              "en": "Which machine learning workload is best for grouping shoppers with similar purchase behavior when no categories are provided?",
              "de": "Welcher Machine-Learning-Workload eignet sich am besten, um Käufer mit ähnlichem Kaufverhalten zu gruppieren, wenn keine Kategorien vorgegeben sind?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Classification",
                      "de": "Klassifizierung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Clustering",
                      "de": "Clustering"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Clustering finds natural groupings in unlabeled data.",
              "de": "Clustering findet natürliche Gruppierungen in nicht gekennzeichneten Daten."
          },
          "hint": {
              "en": "Choose the workload used when no correct labels exist yet.",
              "de": "Wählen Sie den Workload, der verwendet wird, wenn noch keine richtigen Labels vorhanden sind."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/what-are-ai-services"
      },
      {
          "id": "ai900-213",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which AI capability is used to extract printed text from a scanned invoice image?",
              "de": "Welche KI-Fähigkeit wird verwendet, um gedruckten Text aus einem gescannten Rechnungsbild zu extrahieren?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Translation",
                      "de": "Übersetzung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Summarization",
                      "de": "Zusammenfassung"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "Optical character recognition converts visible text in images into machine-readable text.",
              "de": "Die optische Zeichenerkennung wandelt sichtbaren Text in Bildern in maschinenlesbaren Text um."
          },
          "hint": {
              "en": "Think about reading characters from an image.",
              "de": "Denken Sie an das Lesen von Zeichen aus einem Bild."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview"
      },
      {
          "id": "ai900-214",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which AI workload best fits analyzing support-call transcripts to identify customer sentiment?",
              "de": "Welcher KI-Workload eignet sich am besten, um Transkripte von Supportanrufen zu analysieren und die Stimmung von Kunden zu erkennen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Natural language processing",
                      "de": "Natural Language Processing"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Regression",
                      "de": "Regression"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Face detection",
                      "de": "Gesichtserkennung"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Sentiment analysis works on text and is part of natural language processing.",
              "de": "Die Sentimentanalyse arbeitet mit Text und ist Teil von Natural Language Processing."
          },
          "hint": {
              "en": "Pick the workload centered on language rather than images or numbers.",
              "de": "Wählen Sie den Workload, der sich auf Sprache statt auf Bilder oder Zahlen konzentriert."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview"
      },
      {
          "id": "ai900-215",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A hospital is testing AI-assisted triage for incoming patients.",
              "de": "Ein Krankenhaus testet KI-gestützte Triage für eingehende Patienten."
          },
          "question": {
              "en": "Which responsible AI principle is reinforced when a doctor must review an AI triage recommendation before treatment is planned?",
              "de": "Welches Prinzip der verantwortungsvollen KI wird gestärkt, wenn ein Arzt eine KI-Triage-Empfehlung vor der Behandlungsplanung überprüfen muss?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Fairness",
                      "de": "Fairness"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Accountability",
                      "de": "Verantwortlichkeit"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Privacy and security",
                      "de": "Datenschutz und Sicherheit"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Transparency",
                      "de": "Transparenz"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Accountability means people remain responsible for decisions and oversight, especially in high-impact scenarios.",
              "de": "Verantwortlichkeit bedeutet, dass Menschen für Entscheidungen und Aufsicht verantwortlich bleiben, insbesondere in Szenarien mit hoher Auswirkung."
          },
          "hint": {
              "en": "Think about who stays responsible when AI is used.",
              "de": "Denken Sie daran, wer verantwortlich bleibt, wenn KI eingesetzt wird."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2"
      },
      {
          "id": "ai900-216",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which AI workload focuses on identifying faces or objects in photos?",
              "de": "Welcher KI-Workload konzentriert sich auf das Erkennen von Gesichtern oder Objekten in Fotos?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Computer vision",
                      "de": "Computer Vision"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Regression",
                      "de": "Regression"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Translation",
                      "de": "Übersetzung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Clustering",
                      "de": "Clustering"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Computer vision analyzes visual content such as images and video.",
              "de": "Computer Vision analysiert visuelle Inhalte wie Bilder und Videos."
          },
          "hint": {
              "en": "Choose the workload that works with pixels.",
              "de": "Wählen Sie den Workload, der mit Pixeln arbeitet."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/what-are-ai-services"
      },
      {
          "id": "ai900-217",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which AI workload best fits a bot that answers spoken customer questions in natural language?",
              "de": "Welcher KI-Workload eignet sich am besten für einen Bot, der gesprochene Kundenfragen in natürlicher Sprache beantwortet?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Conversational AI",
                      "de": "Konversationelle KI"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Forecasting",
                      "de": "Prognose"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Conversational AI combines language understanding and response generation for interactive assistants.",
              "de": "Konversationelle KI kombiniert Sprachverständnis und Antwortgenerierung für interaktive Assistenten."
          },
          "hint": {
              "en": "Look for an interactive chat or voice assistant workload.",
              "de": "Suchen Sie nach einem interaktiven Chat- oder Sprachassistenten-Workload."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/what-are-ai-services"
      },
      {
          "id": "ai900-218",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A factory collects sensor readings every second from conveyor-belt motors.",
              "de": "Eine Fabrik erfasst jede Sekunde Sensorwerte von Förderbandmotoren."
          },
          "question": {
              "en": "Which AI workload is best for flagging unusual spikes in machine telemetry from a factory line?",
              "de": "Welcher KI-Workload eignet sich am besten, um ungewöhnliche Spitzen in Maschinentelemetrie einer Produktionslinie zu kennzeichnen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Anomaly detection",
                      "de": "Anomalieerkennung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Translation",
                      "de": "Übersetzung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Image classification",
                      "de": "Bildklassifizierung"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Anomaly detection is designed to detect unusual patterns or outliers in data streams.",
              "de": "Die Anomalieerkennung ist dafür ausgelegt, ungewöhnliche Muster oder Ausreißer in Datenströmen zu erkennen."
          },
          "hint": {
              "en": "Choose the workload that looks for rare or abnormal behavior.",
              "de": "Wählen Sie den Workload, der nach seltenem oder abnormalem Verhalten sucht."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/what-are-ai-services"
      },
      {
          "id": "ai900-219",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which responsible AI principle is supported by adding captions, keyboard navigation, and screen-reader-friendly layouts to an AI app?",
              "de": "Welches Prinzip der verantwortungsvollen KI wird unterstützt, wenn einer KI-App Untertitel, Tastaturnavigation und screenreaderfreundliche Layouts hinzugefügt werden?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Privacy and security",
                      "de": "Datenschutz und Sicherheit"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Transparency",
                      "de": "Transparenz"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Inclusiveness",
                      "de": "Inklusivität"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Accountability",
                      "de": "Verantwortlichkeit"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "Accessibility features help a broader range of people use the system effectively, which supports inclusiveness.",
              "de": "Barrierefreiheitsfunktionen helfen einer breiteren Gruppe von Menschen, das System effektiv zu nutzen, was die Inklusivität unterstützt."
          },
          "hint": {
              "en": "Think about designing for users with different needs.",
              "de": "Denken Sie an ein Design für Benutzer mit unterschiedlichen Bedürfnissen."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2"
      },
      {
          "id": "ai900-220",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which responsible AI principle is emphasized when a business shows which factors most influenced a churn prediction?",
              "de": "Welches Prinzip der verantwortungsvollen KI wird betont, wenn ein Unternehmen zeigt, welche Faktoren eine Abwanderungsvorhersage am stärksten beeinflusst haben?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Transparency",
                      "de": "Transparenz"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Fairness",
                      "de": "Fairness"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Privacy and security",
                      "de": "Datenschutz und Sicherheit"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Inclusiveness",
                      "de": "Inklusivität"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Explaining which inputs affected an outcome helps make the model more understandable and transparent.",
              "de": "Zu erklären, welche Eingaben ein Ergebnis beeinflusst haben, macht das Modell verständlicher und transparenter."
          },
          "hint": {
              "en": "Pick the principle about understanding system behavior.",
              "de": "Wählen Sie das Prinzip, das das Verständnis des Systemverhaltens betrifft."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2"
      },
      {
          "id": "ai900-221",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A lender found that approval rates differed sharply across demographic groups.",
              "de": "Ein Kreditgeber stellte fest, dass sich die Genehmigungsraten zwischen demografischen Gruppen stark unterscheiden."
          },
          "question": {
              "en": "Which responsible AI principle is being addressed by rebalancing training data to reduce unequal loan-approval outcomes between groups?",
              "de": "Welches Prinzip der verantwortungsvollen KI wird angesprochen, wenn Trainingsdaten neu ausbalanciert werden, um ungleiche Kreditgenehmigungsergebnisse zwischen Gruppen zu verringern?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Fairness",
                      "de": "Fairness"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Reliability and safety",
                      "de": "Zuverlässigkeit und Sicherheit"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Transparency",
                      "de": "Transparenz"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Privacy and security",
                      "de": "Datenschutz und Sicherheit"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Fairness aims to reduce harmful bias and improve equitable treatment across groups.",
              "de": "Fairness zielt darauf ab, schädliche Verzerrungen zu verringern und eine gerechtere Behandlung über Gruppen hinweg zu verbessern."
          },
          "hint": {
              "en": "Focus on bias reduction across demographics.",
              "de": "Konzentrieren Sie sich auf die Verringerung von Verzerrungen zwischen demografischen Gruppen."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2"
      },
      {
          "id": "ai900-222",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which machine learning workload is used to forecast daily energy consumption?",
              "de": "Welcher Machine-Learning-Workload wird verwendet, um den täglichen Energieverbrauch vorherzusagen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Regression",
                      "de": "Regression"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Translation",
                      "de": "Übersetzung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Clustering",
                      "de": "Clustering"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Forecasting numeric values over time is a regression problem.",
              "de": "Die Vorhersage numerischer Werte über die Zeit ist ein Regressionsproblem."
          },
          "hint": {
              "en": "The answer should produce a number.",
              "de": "Die Antwort sollte eine Zahl liefern."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/what-are-ai-services"
      },
      {
          "id": "ai900-223",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which AI workload includes translating text between languages?",
              "de": "Welcher KI-Workload umfasst die Übersetzung von Text zwischen Sprachen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Computer vision",
                      "de": "Computer Vision"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Forecasting",
                      "de": "Prognose"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Natural language processing",
                      "de": "Natural Language Processing"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Clustering",
                      "de": "Clustering"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "Translation is an NLP task because it works with human language.",
              "de": "Übersetzung ist eine NLP-Aufgabe, weil sie mit menschlicher Sprache arbeitet."
          },
          "hint": {
              "en": "Choose the workload focused on text and language.",
              "de": "Wählen Sie den Workload, der sich auf Text und Sprache konzentriert."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview"
      },
      {
          "id": "ai900-224",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A school launches an online tutor that responds to student questions at any hour.",
              "de": "Eine Schule startet einen Online-Tutor, der rund um die Uhr auf Schülerfragen antwortet."
          },
          "question": {
              "en": "Which responsible AI principle is most directly supported by clearly telling students they are interacting with an AI tutor rather than a human teacher?",
              "de": "Welches Prinzip der verantwortungsvollen KI wird am direktesten unterstützt, wenn Schülern klar gesagt wird, dass sie mit einem KI-Tutor statt mit einem menschlichen Lehrer interagieren?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Fairness",
                      "de": "Fairness"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Transparency",
                      "de": "Transparenz"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Reliability and safety",
                      "de": "Zuverlässigkeit und Sicherheit"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Inclusiveness",
                      "de": "Inklusivität"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Users should understand when AI is involved and how the system is operating, which is a transparency concern.",
              "de": "Benutzer sollten verstehen, wann KI beteiligt ist und wie das System arbeitet, was ein Transparenzthema ist."
          },
          "hint": {
              "en": "Think about being open and clear about AI use.",
              "de": "Denken Sie daran, offen und klar über den Einsatz von KI zu sein."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai?view=azureml-api-2"
      },
      {
          "id": "ai900-225",
          "type": "single",
          "domain": {
              "en": "Describe AI Workloads and Considerations",
              "de": "KI-Workloads und Überlegungen beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which AI capability converts spoken words into text for live meeting captions?",
              "de": "Welche KI-Fähigkeit wandelt gesprochene Wörter für Live-Untertitel in Meetings in Text um?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Speech to text",
                      "de": "Sprache-zu-Text"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Regression",
                      "de": "Regression"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Anomaly detection",
                      "de": "Anomalieerkennung"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Speech to text recognizes spoken audio and transcribes it into written text.",
              "de": "Sprache-zu-Text erkennt gesprochene Audiodaten und transkribiert sie in geschriebenen Text."
          },
          "hint": {
              "en": "Choose the capability that listens and transcribes.",
              "de": "Wählen Sie die Fähigkeit, die zuhört und transkribiert."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/speech-service/overview"
      },
      {
          "id": "ai900-226",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which machine learning workload predicts whether a customer will renew a subscription?",
              "de": "Welcher Machine-Learning-Workload sagt voraus, ob ein Kunde ein Abonnement verlängern wird?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Classification",
                      "de": "Klassifizierung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Regression",
                      "de": "Regression"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Clustering",
                      "de": "Clustering"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Renew or not renew is a category, so this is a classification problem.",
              "de": "Verlängern oder nicht verlängern ist eine Kategorie, daher handelt es sich um ein Klassifizierungsproblem."
          },
          "hint": {
              "en": "Look for a yes-or-no label prediction.",
              "de": "Suchen Sie nach einer Ja-oder-Nein-Labelvorhersage."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning?view=azureml-api-2"
      },
      {
          "id": "ai900-227",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which machine learning workload predicts the number of support tickets expected next week?",
              "de": "Welcher Machine-Learning-Workload sagt die Anzahl der zu erwartenden Supporttickets in der nächsten Woche voraus?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Clustering",
                      "de": "Clustering"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Regression",
                      "de": "Regression"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Classification",
                      "de": "Klassifizierung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Named entity recognition",
                      "de": "Erkennung benannter Entitäten"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Regression predicts numeric values such as counts, costs, or durations.",
              "de": "Regression sagt numerische Werte wie Anzahl, Kosten oder Dauer voraus."
          },
          "hint": {
              "en": "The answer should output a number.",
              "de": "Die Antwort sollte eine Zahl ausgeben."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning?view=azureml-api-2"
      },
      {
          "id": "ai900-228",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which machine learning workload groups IoT devices based on similar behavior when no labels are available?",
              "de": "Welcher Machine-Learning-Workload gruppiert IoT-Geräte anhand ähnlichen Verhaltens, wenn keine Labels verfügbar sind?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Regression",
                      "de": "Regression"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Clustering",
                      "de": "Clustering"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Classification",
                      "de": "Klassifizierung"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "Clustering is used for unlabeled data when you want to discover groups.",
              "de": "Clustering wird für nicht gekennzeichnete Daten verwendet, wenn Gruppen entdeckt werden sollen."
          },
          "hint": {
              "en": "No labels usually points to clustering.",
              "de": "Keine Labels deuten in der Regel auf Clustering hin."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning?view=azureml-api-2"
      },
      {
          "id": "ai900-229",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A retailer has historical sales data and wants quick experimentation without hand-testing many algorithms.",
              "de": "Ein Einzelhändler verfügt über historische Verkaufsdaten und möchte schnell experimentieren, ohne viele Algorithmen manuell zu testen."
          },
          "question": {
              "en": "Which Azure Machine Learning capability should you use to automatically try multiple algorithms and settings for a labeled sales dataset?",
              "de": "Welche Funktion von Azure Machine Learning sollten Sie verwenden, um automatisch mehrere Algorithmen und Einstellungen für einen gelabelten Verkaufsdatensatz auszuprobieren?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Automated ML",
                      "de": "Automated ML"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Face detection",
                      "de": "Gesichtserkennung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Translator",
                      "de": "Translator"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Automated ML explores model and preprocessing combinations to find a strong model with less manual trial and error.",
              "de": "Automated ML untersucht Modell- und Vorverarbeitungskombinationen, um mit weniger manuellem Ausprobieren ein starkes Modell zu finden."
          },
          "hint": {
              "en": "Choose the Azure ML feature that automates model search.",
              "de": "Wählen Sie die Azure-ML-Funktion, die die Modellsuche automatisiert."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/concept-automated-ml?view=azureml-api-2"
      },
      {
          "id": "ai900-230",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "In a machine learning dataset, what is the label?",
              "de": "Was ist in einem Machine-Learning-Datensatz das Label?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "An input variable used for prediction",
                      "de": "Eine Eingabevariable für die Vorhersage"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "The value the model is trying to predict",
                      "de": "Der Wert, den das Modell vorhersagen soll"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "A group produced by clustering",
                      "de": "Eine durch Clustering erzeugte Gruppe"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "A deployed API endpoint",
                      "de": "Ein bereitgestellter API-Endpunkt"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "The label is the known outcome in training data that the model learns to predict.",
              "de": "Das Label ist das bekannte Ergebnis in den Trainingsdaten, das das Modell zu prognostizieren lernt."
          },
          "hint": {
              "en": "Think of the label as the target column.",
              "de": "Betrachten Sie das Label als Zielspalte."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning?view=azureml-api-2"
      },
      {
          "id": "ai900-231",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "What is inferencing in machine learning?",
              "de": "Was ist Inferenz im Machine Learning?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Using a trained model to make predictions on new data",
                      "de": "Verwendung eines trainierten Modells, um Vorhersagen für neue Daten zu treffen"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Removing rows with missing values",
                      "de": "Entfernen von Zeilen mit fehlenden Werten"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Creating labels manually",
                      "de": "Manuelles Erstellen von Labels"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Grouping unlabeled data",
                      "de": "Gruppieren nicht gekennzeichneter Daten"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Inferencing happens after training, when a model is applied to unseen data to produce an output.",
              "de": "Inferenz erfolgt nach dem Training, wenn ein Modell auf unbekannte Daten angewendet wird, um ein Ergebnis zu erzeugen."
          },
          "hint": {
              "en": "Think about the model being used, not trained.",
              "de": "Denken Sie daran, dass das Modell verwendet und nicht trainiert wird."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning?view=azureml-api-2"
      },
      {
          "id": "ai900-232",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A team evaluates a churn model and sees excellent results on the training set but disappointing results in production trials.",
              "de": "Ein Team bewertet ein Abwanderungsmodell und sieht hervorragende Ergebnisse im Trainingssatz, aber enttäuschende Ergebnisse in Produktionstests."
          },
          "question": {
              "en": "What problem is indicated when a model performs very well on training data but poorly on new data?",
              "de": "Welches Problem liegt vor, wenn ein Modell auf Trainingsdaten sehr gut, auf neuen Daten jedoch schlecht abschneidet?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Underfitting",
                      "de": "Underfitting"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Overfitting",
                      "de": "Overfitting"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Translation",
                      "de": "Übersetzung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Overfitting means the model learned training specifics too closely and does not generalize well.",
              "de": "Overfitting bedeutet, dass das Modell die Besonderheiten der Trainingsdaten zu stark gelernt hat und nicht gut verallgemeinert."
          },
          "hint": {
              "en": "Strong training performance alone is not enough.",
              "de": "Eine starke Trainingsleistung allein reicht nicht aus."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning?view=azureml-api-2"
      },
      {
          "id": "ai900-233",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "What is the main purpose of a test dataset in machine learning?",
              "de": "Was ist der Hauptzweck eines Testdatensatzes im Machine Learning?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "To label the data manually",
                      "de": "Um die Daten manuell zu labeln"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "To train the model weights",
                      "de": "Um die Modellgewichte zu trainieren"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "To evaluate performance on unseen data",
                      "de": "Um die Leistung auf unbekannten Daten zu bewerten"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "To generate embeddings",
                      "de": "Um Embeddings zu erzeugen"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "A test dataset estimates how well the trained model will generalize to new data.",
              "de": "Ein Testdatensatz schätzt, wie gut das trainierte Modell auf neue Daten verallgemeinert."
          },
          "hint": {
              "en": "Choose the dataset used after training for evaluation.",
              "de": "Wählen Sie den Datensatz, der nach dem Training zur Bewertung verwendet wird."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning?view=azureml-api-2"
      },
      {
          "id": "ai900-234",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which Azure service provides ML workspaces, compute resources, model management, and deployment endpoints?",
              "de": "Welcher Azure-Dienst bietet ML-Arbeitsbereiche, Computeressourcen, Modellverwaltung und Bereitstellungsendpunkte?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Azure AI Vision",
                      "de": "Azure AI Vision"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Azure AI Language",
                      "de": "Azure AI Language"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Azure AI Search",
                      "de": "Azure AI Search"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Azure Machine Learning",
                      "de": "Azure Machine Learning"
                  }
              }
          ],
          "correct": [
              "D"
          ],
          "explanation": {
              "en": "Azure Machine Learning is the platform service for building, training, tracking, and deploying ML models on Azure.",
              "de": "Azure Machine Learning ist der Plattformdienst zum Erstellen, Trainieren, Nachverfolgen und Bereitstellen von ML-Modellen in Azure."
          },
          "hint": {
              "en": "Pick the dedicated ML platform service.",
              "de": "Wählen Sie den dedizierten ML-Plattformdienst."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning?view=azureml-api-2"
      },
      {
          "id": "ai900-235",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A data science group wants Azure to run broad model experiments overnight on a tabular dataset.",
              "de": "Eine Data-Science-Gruppe möchte, dass Azure über Nacht umfangreiche Modelleksperimente auf einem tabellarischen Datensatz ausführt."
          },
          "question": {
              "en": "Which Azure Machine Learning feature is designed to compare many preprocessing methods and algorithms automatically?",
              "de": "Welche Funktion von Azure Machine Learning ist dafür ausgelegt, viele Vorverarbeitungsmethoden und Algorithmen automatisch zu vergleichen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Automated ML",
                      "de": "Automated ML"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Language detection",
                      "de": "Spracherkennung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Face service",
                      "de": "Face-Dienst"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Automated ML searches across candidate pipelines so teams can identify promising models faster.",
              "de": "Automated ML durchsucht Kandidaten-Pipelines, damit Teams schneller vielversprechende Modelle identifizieren können."
          },
          "hint": {
              "en": "The keyword is automatic experimentation.",
              "de": "Das Schlüsselwort ist automatisches Experimentieren."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/concept-automated-ml?view=azureml-api-2"
      },
      {
          "id": "ai900-236",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which is an example of a binary classification outcome?",
              "de": "Welches ist ein Beispiel für das Ergebnis einer binären Klassifizierung?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Approve or reject a loan",
                      "de": "Kredit genehmigen oder ablehnen"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Predict the price of a house",
                      "de": "Preis eines Hauses vorhersagen"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Group customers into segments",
                      "de": "Kunden in Segmente gruppieren"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Extract text from a form",
                      "de": "Text aus einem Formular extrahieren"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Binary classification predicts one of two possible classes.",
              "de": "Die binäre Klassifizierung sagt eine von zwei möglichen Klassen voraus."
          },
          "hint": {
              "en": "Look for exactly two possible labels.",
              "de": "Suchen Sie nach genau zwei möglichen Labels."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning?view=azureml-api-2"
      },
      {
          "id": "ai900-237",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which is an example of a regression output?",
              "de": "Welches ist ein Beispiel für die Ausgabe einer Regression?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Spam or not spam",
                      "de": "Spam oder kein Spam"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Predicted home price",
                      "de": "Vorhergesagter Hauspreis"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Customer segment number",
                      "de": "Kundensegmentnummer"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Language detected",
                      "de": "Erkannte Sprache"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Regression outputs a continuous numeric value, such as a price.",
              "de": "Regression liefert einen kontinuierlichen numerischen Wert, etwa einen Preis."
          },
          "hint": {
              "en": "Choose the option that returns a measured amount.",
              "de": "Wählen Sie die Option, die eine messbare Größe zurückgibt."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning?view=azureml-api-2"
      },
      {
          "id": "ai900-238",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A fraud team wants to explore patterns in millions of transactions before any cases are confirmed.",
              "de": "Ein Betrugsteam möchte Muster in Millionen von Transaktionen untersuchen, bevor Fälle bestätigt sind."
          },
          "question": {
              "en": "Which machine learning technique is best when analysts want to group similar transactions but do not have fraud labels?",
              "de": "Welche Machine-Learning-Technik ist am besten geeignet, wenn Analysten ähnliche Transaktionen gruppieren möchten, aber keine Betrugslabels haben?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Regression",
                      "de": "Regression"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Named entity recognition",
                      "de": "Erkennung benannter Entitäten"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Clustering",
                      "de": "Clustering"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "Clustering helps discover structure in unlabeled data by grouping similar items together.",
              "de": "Clustering hilft, Strukturen in nicht gekennzeichneten Daten zu erkennen, indem ähnliche Elemente zusammengefasst werden."
          },
          "hint": {
              "en": "No labels means you should think unsupervised learning.",
              "de": "Keine Labels bedeuten, dass Sie an unüberwachtes Lernen denken sollten."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning?view=azureml-api-2"
      },
      {
          "id": "ai900-239",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "In machine learning, what is a feature?",
              "de": "Was ist im Machine Learning ein Feature?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "An input variable used by the model",
                      "de": "Eine vom Modell verwendete Eingabevariable"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "The final prediction target",
                      "de": "Das endgültige Vorhersageziel"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "A model deployment endpoint",
                      "de": "Ein Bereitstellungsendpunkt für ein Modell"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "A responsible AI principle",
                      "de": "Ein Prinzip der verantwortungsvollen KI"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Features are the inputs the model uses to learn patterns and make predictions.",
              "de": "Features sind die Eingaben, die das Modell nutzt, um Muster zu lernen und Vorhersagen zu treffen."
          },
          "hint": {
              "en": "Think of columns that describe each row.",
              "de": "Denken Sie an Spalten, die jede Zeile beschreiben."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning?view=azureml-api-2"
      },
      {
          "id": "ai900-240",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "After training a model in Azure Machine Learning, what should you deploy so applications can request predictions?",
              "de": "Was sollten Sie nach dem Trainieren eines Modells in Azure Machine Learning bereitstellen, damit Anwendungen Vorhersagen anfordern können?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "A language project",
                      "de": "Ein Sprachprojekt"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "A scanner driver",
                      "de": "Einen Scannertreiber"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "A content filter policy only",
                      "de": "Nur eine Content-Filter-Richtlinie"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "An inference endpoint",
                      "de": "Einen Inferenzendpunkt"
                  }
              }
          ],
          "correct": [
              "D"
          ],
          "explanation": {
              "en": "Applications typically consume a trained model through a deployed inference endpoint.",
              "de": "Anwendungen nutzen ein trainiertes Modell typischerweise über einen bereitgestellten Inferenzendpunkt."
          },
          "hint": {
              "en": "Think about how apps call a model after training.",
              "de": "Denken Sie daran, wie Apps ein Modell nach dem Training aufrufen."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning?view=azureml-api-2"
      },
      {
          "id": "ai900-241",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A finance team wants an Azure model to estimate future revenue using historical trends.",
              "de": "Ein Finanzteam möchte ein Azure-Modell, das zukünftige Umsätze anhand historischer Trends schätzt."
          },
          "question": {
              "en": "Which type of machine learning problem is predicting next month's sales from historical monthly sales data?",
              "de": "Welcher Typ von Machine-Learning-Problem ist die Vorhersage der Verkäufe des nächsten Monats auf Basis historischer Monatsdaten?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Clustering",
                      "de": "Clustering"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Regression",
                      "de": "Regression"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Sales forecasting predicts a numeric quantity, which makes it a regression task.",
              "de": "Die Absatzprognose sagt eine numerische Größe voraus und ist daher eine Regressionsaufgabe."
          },
          "hint": {
              "en": "Forecasting a number still maps to regression.",
              "de": "Die Vorhersage einer Zahl gehört weiterhin zur Regression."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning?view=azureml-api-2"
      },
      {
          "id": "ai900-242",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "What is the process called when a model learns patterns from historical data?",
              "de": "Wie heißt der Prozess, wenn ein Modell Muster aus historischen Daten lernt?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Training",
                      "de": "Training"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Translation",
                      "de": "Übersetzung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Captioning",
                      "de": "Beschriftungserstellung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Deployment",
                      "de": "Bereitstellung"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Training is the stage where the algorithm fits patterns using known data.",
              "de": "Training ist die Phase, in der der Algorithmus Muster anhand bekannter Daten anpasst."
          },
          "hint": {
              "en": "This happens before inference and deployment.",
              "de": "Dies geschieht vor Inferenz und Bereitstellung."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning?view=azureml-api-2"
      },
      {
          "id": "ai900-243",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": null,
          "question": {
              "en": "Which Azure Machine Learning compute resource can scale out to run submitted training jobs on demand?",
              "de": "Welche Computeressource in Azure Machine Learning kann bei Bedarf horizontal skalieren, um eingereichte Trainingsjobs auszuführen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Compute instance",
                      "de": "Compute-Instanz"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Translator resource",
                      "de": "Translator-Ressource"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Compute cluster",
                      "de": "Compute-Cluster"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Question answering project",
                      "de": "Question-Answering-Projekt"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "Compute clusters are shared scalable resources designed to run training workloads.",
              "de": "Compute-Cluster sind gemeinsam genutzte skalierbare Ressourcen, die für Trainingsworkloads ausgelegt sind."
          },
          "hint": {
              "en": "Pick the option built for elastic training capacity.",
              "de": "Wählen Sie die Option, die für elastische Trainingskapazität ausgelegt ist."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning?view=azureml-api-2"
      },
      {
          "id": "ai900-244",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A business analyst opens Azure Machine Learning studio with a clean tabular dataset and little Python experience.",
              "de": "Ein Business-Analyst öffnet Azure Machine Learning Studio mit einem sauberen tabellarischen Datensatz und wenig Python-Erfahrung."
          },
          "question": {
              "en": "Which Azure Machine Learning capability is best for a business analyst with tabular data who wants guided model creation with minimal coding?",
              "de": "Welche Funktion von Azure Machine Learning eignet sich am besten für einen Business-Analysten mit tabellarischen Daten, der eine geführte Modellerstellung mit minimalem Code möchte?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Automated ML",
                      "de": "Automated ML"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Face API",
                      "de": "Face-API"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Automated ML is intended to simplify model training and evaluation for common prediction tasks.",
              "de": "Automated ML soll das Modelltraining und die Bewertung für gängige Vorhersageaufgaben vereinfachen."
          },
          "hint": {
              "en": "Look for the low-code option inside Azure ML.",
              "de": "Suchen Sie nach der Low-Code-Option in Azure ML."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/concept-automated-ml?view=azureml-api-2"
      },
      {
          "id": "ai900-245",
          "type": "single",
          "domain": {
              "en": "Describe Fundamental Principles of ML on Azure",
              "de": "Grundlegende Prinzipien des ML auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Predicting one of three flower species from sepal and petal measurements is an example of which problem type?",
              "de": "Die Vorhersage einer von drei Blumenarten anhand von Kelch- und Blütenblattmaßen ist ein Beispiel für welchen Problemtyp?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Multiclass classification",
                      "de": "Mehrklassenklassifizierung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Regression",
                      "de": "Regression"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Clustering",
                      "de": "Clustering"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Anomaly detection",
                      "de": "Anomalieerkennung"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "The model must choose one category from more than two classes, which is multiclass classification.",
              "de": "Das Modell muss eine Kategorie aus mehr als zwei Klassen auswählen, was eine Mehrklassenklassifizierung ist."
          },
          "hint": {
              "en": "There are several discrete labels, not a number.",
              "de": "Es gibt mehrere diskrete Labels und keine Zahl."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning?view=azureml-api-2"
      },
      {
          "id": "ai900-246",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which computer vision task assigns one label such as dog, cat, or bird to an entire image?",
              "de": "Welche Computer-Vision-Aufgabe weist einem gesamten Bild ein Label wie Hund, Katze oder Vogel zu?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Image classification",
                      "de": "Bildklassifizierung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Speech synthesis",
                      "de": "Sprachsynthese"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Image classification predicts the category of the whole image.",
              "de": "Die Bildklassifizierung sagt die Kategorie des gesamten Bildes voraus."
          },
          "hint": {
              "en": "Look for a single label for the entire picture.",
              "de": "Suchen Sie nach einem einzelnen Label für das gesamte Bild."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview"
      },
      {
          "id": "ai900-247",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which computer vision task identifies each forklift in a warehouse photo and returns its location?",
              "de": "Welche Computer-Vision-Aufgabe erkennt jeden Gabelstapler in einem Lagerfoto und gibt dessen Position zurück?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Image classification",
                      "de": "Bildklassifizierung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Translation",
                      "de": "Übersetzung"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Object detection identifies objects and their positions, typically with bounding boxes.",
              "de": "Die Objekterkennung identifiziert Objekte und ihre Positionen, typischerweise mit Begrenzungsrahmen."
          },
          "hint": {
              "en": "The question asks for both what and where.",
              "de": "Die Frage verlangt sowohl was als auch wo."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview"
      },
      {
          "id": "ai900-248",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which computer vision capability extracts text from a photo of a receipt?",
              "de": "Welche Computer-Vision-Fähigkeit extrahiert Text aus einem Foto eines Kassenbons?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Image tagging",
                      "de": "Bildtagging"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Face detection",
                      "de": "Gesichtserkennung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Clustering",
                      "de": "Clustering"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "OCR reads visible text in images and converts it to digital text.",
              "de": "OCR liest sichtbaren Text in Bildern und wandelt ihn in digitalen Text um."
          },
          "hint": {
              "en": "Choose the option for reading characters from images.",
              "de": "Wählen Sie die Option zum Lesen von Zeichen aus Bildern."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview"
      },
      {
          "id": "ai900-249",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A retailer wants to recognize its new package designs on store shelves.",
              "de": "Ein Einzelhändler möchte seine neuen Verpackungsdesigns in Ladenregalen erkennen."
          },
          "question": {
              "en": "Which Azure service is best for training a vision model on your company's own product-packaging images?",
              "de": "Welcher Azure-Dienst eignet sich am besten, um ein Vision-Modell auf den Produktverpackungsbildern Ihres Unternehmens zu trainieren?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Translator",
                      "de": "Translator"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Speech service",
                      "de": "Sprachdienst"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Question answering",
                      "de": "Question Answering"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Custom Vision",
                      "de": "Custom Vision"
                  }
              }
          ],
          "correct": [
              "D"
          ],
          "explanation": {
              "en": "Custom Vision is designed for training image classifiers and detectors on your own labeled images.",
              "de": "Custom Vision ist dafür ausgelegt, Bildklassifikatoren und -detektoren mit Ihren eigenen gelabelten Bildern zu trainieren."
          },
          "hint": {
              "en": "Look for the service made for custom image categories.",
              "de": "Suchen Sie nach dem Dienst für benutzerdefinierte Bildkategorien."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/overview"
      },
      {
          "id": "ai900-250",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which Azure AI capability can generate tags and a caption for a general photograph?",
              "de": "Welche Azure-AI-Fähigkeit kann Tags und eine Beschreibung für ein allgemeines Foto erzeugen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Image analysis",
                      "de": "Bildanalyse"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "CLU",
                      "de": "CLU"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Automated ML",
                      "de": "Automated ML"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Translator",
                      "de": "Translator"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Image analysis can describe scenes and identify common visual elements in photos.",
              "de": "Die Bildanalyse kann Szenen beschreiben und häufige visuelle Elemente in Fotos identifizieren."
          },
          "hint": {
              "en": "Think about a general-purpose vision service for descriptions.",
              "de": "Denken Sie an einen allgemeinen Vision-Dienst für Beschreibungen."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview"
      },
      {
          "id": "ai900-251",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which Azure AI service is specialized for detecting and analyzing human faces?",
              "de": "Welcher Azure-AI-Dienst ist auf das Erkennen und Analysieren menschlicher Gesichter spezialisiert?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Question answering",
                      "de": "Question Answering"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Face service",
                      "de": "Face-Dienst"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Automated ML",
                      "de": "Automated ML"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Translator",
                      "de": "Translator"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "The Face service is designed specifically for face-related analysis tasks.",
              "de": "Der Face-Dienst ist speziell für gesichtsbezogene Analyseaufgaben ausgelegt."
          },
          "hint": {
              "en": "Pick the service dedicated to faces.",
              "de": "Wählen Sie den Dienst, der speziell für Gesichter vorgesehen ist."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview-identity"
      },
      {
          "id": "ai900-252",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A mobile app analyzes photos from a garage camera to guide drivers.",
              "de": "Eine mobile App analysiert Fotos einer Garagenkamera, um Fahrer zu leiten."
          },
          "question": {
              "en": "Which vision task should you use when a parking app needs the location of each open parking space in an image?",
              "de": "Welche Vision-Aufgabe sollten Sie verwenden, wenn eine Parkplatz-App die Position jedes freien Parkplatzes in einem Bild benötigt?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Image captioning",
                      "de": "Bildbeschreibung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Speech to text",
                      "de": "Sprache-zu-Text"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Key phrase extraction",
                      "de": "Schlüsselbegriffextraktion"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "Object detection is used when you need to locate multiple targets within the image.",
              "de": "Objekterkennung wird verwendet, wenn mehrere Ziele innerhalb des Bildes lokalisiert werden sollen."
          },
          "hint": {
              "en": "The requirement includes position information.",
              "de": "Die Anforderung umfasst Positionsinformationen."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview"
      },
      {
          "id": "ai900-253",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which capability should you use to read text from a handwritten maintenance form?",
              "de": "Welche Fähigkeit sollten Sie verwenden, um Text aus einem handschriftlichen Wartungsformular zu lesen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Image classification",
                      "de": "Bildklassifizierung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Sentiment analysis",
                      "de": "Sentimentanalyse"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "OCR is the correct choice for extracting text, including from scanned handwritten content.",
              "de": "OCR ist die richtige Wahl, um Text zu extrahieren, auch aus gescannten handschriftlichen Inhalten."
          },
          "hint": {
              "en": "Choose the feature that reads visible text.",
              "de": "Wählen Sie die Funktion, die sichtbaren Text liest."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview"
      },
      {
          "id": "ai900-254",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which computer vision task returns one category for an image instead of locations for multiple objects?",
              "de": "Welche Computer-Vision-Aufgabe liefert eine Kategorie für ein Bild statt Positionen für mehrere Objekte?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Image classification",
                      "de": "Bildklassifizierung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Face detection",
                      "de": "Gesichtserkennung"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Image classification answers 'what is this image mostly about?' while object detection answers 'what objects are where?'.",
              "de": "Die Bildklassifizierung beantwortet 'Worum geht es in diesem Bild hauptsächlich?', während die Objekterkennung 'Welche Objekte sind wo?' beantwortet."
          },
          "hint": {
              "en": "No locations usually means classification.",
              "de": "Keine Positionen bedeuten in der Regel Klassifizierung."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview"
      },
      {
          "id": "ai900-255",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A factory has collected photos of scratched, cracked, and acceptable parts.",
              "de": "Eine Fabrik hat Fotos von zerkratzten, gerissenen und akzeptablen Teilen gesammelt."
          },
          "question": {
              "en": "Which Azure service is best for a manufacturer that wants to train a custom defect classifier from its own labeled product images?",
              "de": "Welcher Azure-Dienst eignet sich am besten für einen Hersteller, der einen benutzerdefinierten Defektklassifikator aus eigenen gelabelten Produktbildern trainieren möchte?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "CLU",
                      "de": "CLU"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Speech service",
                      "de": "Sprachdienst"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Translator",
                      "de": "Translator"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Custom Vision",
                      "de": "Custom Vision"
                  }
              }
          ],
          "correct": [
              "D"
          ],
          "explanation": {
              "en": "Custom Vision supports custom image classification scenarios with your domain-specific images.",
              "de": "Custom Vision unterstützt benutzerdefinierte Bildklassifizierungsszenarien mit Ihren domänenspezifischen Bildern."
          },
          "hint": {
              "en": "Look for the service intended for custom vision training.",
              "de": "Suchen Sie nach dem Dienst, der für benutzerdefiniertes Vision-Training gedacht ist."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/overview"
      },
      {
          "id": "ai900-256",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which feature can produce captions and tags that summarize what appears in a photo?",
              "de": "Welche Funktion kann Beschriftungen und Tags erzeugen, die zusammenfassen, was auf einem Foto zu sehen ist?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Image analysis",
                      "de": "Bildanalyse"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Language detection",
                      "de": "Spracherkennung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Automated ML",
                      "de": "Automated ML"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Question answering",
                      "de": "Question Answering"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Image analysis provides descriptive metadata such as tags and captions for common scenes.",
              "de": "Die Bildanalyse liefert beschreibende Metadaten wie Tags und Beschriftungen für gängige Szenen."
          },
          "hint": {
              "en": "Think about describing visual content automatically.",
              "de": "Denken Sie daran, visuelle Inhalte automatisch zu beschreiben."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview"
      },
      {
          "id": "ai900-257",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which capability should you use to read the characters on a vehicle license plate from an image?",
              "de": "Welche Fähigkeit sollten Sie verwenden, um die Zeichen auf einem Kfz-Kennzeichen aus einem Bild zu lesen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Face service",
                      "de": "Face-Dienst"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Text summarization",
                      "de": "Textzusammenfassung"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "Reading alphanumeric characters from an image is an OCR task.",
              "de": "Das Lesen alphanumerischer Zeichen aus einem Bild ist eine OCR-Aufgabe."
          },
          "hint": {
              "en": "Pick the text-reading vision feature.",
              "de": "Wählen Sie die textlesende Vision-Funktion."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview"
      },
      {
          "id": "ai900-258",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A corporate security team archives entry-camera images and wants to protect employee privacy.",
              "de": "Ein Unternehmenssicherheitsteam archiviert Bilder von Eingangskameras und möchte die Privatsphäre der Mitarbeiter schützen."
          },
          "question": {
              "en": "Which Azure AI capability should a security team use first if it wants to blur faces before storing building photos?",
              "de": "Welche Azure-AI-Fähigkeit sollte ein Sicherheitsteam zuerst verwenden, wenn es Gesichter unkenntlich machen möchte, bevor Gebäudeaufnahmen gespeichert werden?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Translator",
                      "de": "Translator"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Face detection",
                      "de": "Gesichtserkennung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Question answering",
                      "de": "Question Answering"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Regression",
                      "de": "Regression"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "The faces must be detected before they can be blurred or otherwise processed.",
              "de": "Die Gesichter müssen erkannt werden, bevor sie unkenntlich gemacht oder anderweitig verarbeitet werden können."
          },
          "hint": {
              "en": "First find the faces, then apply the blur.",
              "de": "Zuerst die Gesichter finden, dann die Unschärfe anwenden."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview-identity"
      },
      {
          "id": "ai900-259",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which vision capability can identify dominant colors, common objects, and a short scene description in travel photos?",
              "de": "Welche Vision-Fähigkeit kann dominante Farben, häufige Objekte und eine kurze Szenenbeschreibung in Reisefotos identifizieren?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Image analysis",
                      "de": "Bildanalyse"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "CLU",
                      "de": "CLU"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Speech synthesis",
                      "de": "Sprachsynthese"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Classification of email intent",
                      "de": "Klassifizierung von E-Mail-Absichten"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Image analysis is the general-purpose vision feature for tags, captions, and image attributes.",
              "de": "Die Bildanalyse ist die allgemeine Vision-Funktion für Tags, Beschriftungen und Bildattribute."
          },
          "hint": {
              "en": "This is broader than reading text or detecting just faces.",
              "de": "Dies ist allgemeiner als das Lesen von Text oder das reine Erkennen von Gesichtern."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview"
      },
      {
          "id": "ai900-260",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": null,
          "question": {
              "en": "Which Azure service is most suitable for detecting a company's own logo on store shelves?",
              "de": "Welcher Azure-Dienst eignet sich am besten, um das eigene Firmenlogo in Ladenregalen zu erkennen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Text Analytics",
                      "de": "Text Analytics"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Translator",
                      "de": "Translator"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Speech service",
                      "de": "Sprachdienst"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Custom Vision",
                      "de": "Custom Vision"
                  }
              }
          ],
          "correct": [
              "D"
          ],
          "explanation": {
              "en": "A company-specific logo is a custom visual target, which fits Custom Vision.",
              "de": "Ein firmenspezifisches Logo ist ein benutzerdefiniertes visuelles Ziel und passt daher zu Custom Vision."
          },
          "hint": {
              "en": "The key clue is that the visual category is specific to one business.",
              "de": "Der entscheidende Hinweis ist, dass die visuelle Kategorie unternehmensspezifisch ist."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/overview"
      },
      {
          "id": "ai900-261",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A safety system reviews site photos to confirm workers are wearing protective helmets.",
              "de": "Ein Sicherheitssystem prüft Baustellenfotos, um zu bestätigen, dass Arbeiter Schutzhelme tragen."
          },
          "question": {
              "en": "Which computer vision task should be used to count helmets in a construction image and know where each helmet appears?",
              "de": "Welche Computer-Vision-Aufgabe sollte verwendet werden, um Helme in einem Baustellenbild zu zählen und zu wissen, wo jeder Helm erscheint?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Image classification",
                      "de": "Bildklassifizierung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Language detection",
                      "de": "Spracherkennung"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Object detection locates each instance, enabling counting as well as positioning.",
              "de": "Die Objekterkennung lokalisiert jede Instanz und ermöglicht damit sowohl das Zählen als auch die Positionsbestimmung."
          },
          "hint": {
              "en": "Counting separate items requires locating them individually.",
              "de": "Das Zählen einzelner Elemente erfordert deren individuelle Lokalisierung."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview"
      },
      {
          "id": "ai900-262",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which AI workload primarily works with pixels rather than words or numbers?",
              "de": "Welcher KI-Workload arbeitet primär mit Pixeln statt mit Wörtern oder Zahlen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Computer vision",
                      "de": "Computer Vision"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Regression",
                      "de": "Regression"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Natural language processing",
                      "de": "Natural Language Processing"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Clustering",
                      "de": "Clustering"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Computer vision focuses on interpreting visual content such as images and video.",
              "de": "Computer Vision konzentriert sich auf die Interpretation visueller Inhalte wie Bilder und Videos."
          },
          "hint": {
              "en": "Think about image-based AI.",
              "de": "Denken Sie an bildbasierte KI."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/what-are-ai-services"
      },
      {
          "id": "ai900-263",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which Azure service is best for training a custom model to recognize company-specific product categories in photos?",
              "de": "Welcher Azure-Dienst eignet sich am besten, um ein benutzerdefiniertes Modell zur Erkennung firmenspezifischer Produktkategorien in Fotos zu trainieren?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Azure AI Language",
                      "de": "Azure AI Language"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Azure OpenAI",
                      "de": "Azure OpenAI"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Custom Vision",
                      "de": "Custom Vision"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Azure Translator",
                      "de": "Azure Translator"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "Custom Vision supports custom image categories and company-specific visual scenarios.",
              "de": "Custom Vision unterstützt benutzerdefinierte Bildkategorien und firmenspezifische visuelle Szenarien."
          },
          "hint": {
              "en": "Choose the service made for your own labeled images.",
              "de": "Wählen Sie den Dienst für Ihre eigenen gelabelten Bilder."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/overview"
      },
      {
          "id": "ai900-264",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A legal department is digitizing thousands of paper contracts.",
              "de": "Eine Rechtsabteilung digitalisiert Tausende von Papierverträgen."
          },
          "question": {
              "en": "Which capability should be used to turn scanned PDF pages into searchable text?",
              "de": "Welche Fähigkeit sollte verwendet werden, um gescannte PDF-Seiten in durchsuchbaren Text umzuwandeln?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Speech to text",
                      "de": "Sprache-zu-Text"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Face detection",
                      "de": "Gesichtserkennung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Image classification",
                      "de": "Bildklassifizierung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              }
          ],
          "correct": [
              "D"
          ],
          "explanation": {
              "en": "OCR extracts text from page images so the content can be indexed and searched.",
              "de": "OCR extrahiert Text aus Seitenbildern, damit die Inhalte indiziert und durchsucht werden können."
          },
          "hint": {
              "en": "The goal is searchable text from scanned pages.",
              "de": "Das Ziel ist durchsuchbarer Text aus gescannten Seiten."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview"
      },
      {
          "id": "ai900-265",
          "type": "single",
          "domain": {
              "en": "Describe Features of Computer Vision on Azure",
              "de": "Features von Computer Vision auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which computer vision task returns bounding boxes around detected objects?",
              "de": "Welche Computer-Vision-Aufgabe liefert Begrenzungsrahmen um erkannte Objekte?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Image captioning",
                      "de": "Bildbeschreibung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Speech translation",
                      "de": "Sprachübersetzung"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Bounding boxes are a hallmark of object detection rather than image classification.",
              "de": "Begrenzungsrahmen sind ein Kennzeichen der Objekterkennung und nicht der Bildklassifizierung."
          },
          "hint": {
              "en": "The clue is the location information.",
              "de": "Der Hinweis sind die Positionsinformationen."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/overview"
      },
      {
          "id": "ai900-266",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which Azure AI feature is used to detect sentiment and extract key phrases from customer reviews?",
              "de": "Welche Azure-AI-Funktion wird verwendet, um Stimmung zu erkennen und Schlüsselbegriffe aus Kundenbewertungen zu extrahieren?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Text Analytics in Azure AI Language",
                      "de": "Text Analytics in Azure AI Language"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Custom Vision",
                      "de": "Custom Vision"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Regression",
                      "de": "Regression"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Sentiment analysis and key phrase extraction are core text analytics capabilities.",
              "de": "Sentimentanalyse und Schlüsselbegriffextraktion sind zentrale Text-Analytics-Funktionen."
          },
          "hint": {
              "en": "Look for the language service that analyzes text meaning.",
              "de": "Suchen Sie nach dem Sprachdienst, der Textbedeutung analysiert."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview"
      },
      {
          "id": "ai900-267",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which Azure AI capability maps a user's utterance such as 'book a flight' to an intent?",
              "de": "Welche Azure-AI-Fähigkeit ordnet eine Äußerung wie 'Buche einen Flug' einer Absicht zu?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Conversational Language Understanding",
                      "de": "Conversational Language Understanding"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Image analysis",
                      "de": "Bildanalyse"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Face service",
                      "de": "Face-Dienst"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Conversational Language Understanding identifies intents and entities in user utterances.",
              "de": "Conversational Language Understanding identifiziert Absichten und Entitäten in Benutzeräußerungen."
          },
          "hint": {
              "en": "Pick the service built for intent recognition.",
              "de": "Wählen Sie den Dienst, der für Intent-Erkennung entwickelt wurde."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/overview"
      },
      {
          "id": "ai900-268",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which Azure AI capability is designed to answer FAQ-style questions from a curated knowledge base?",
              "de": "Welche Azure-AI-Fähigkeit ist dafür ausgelegt, FAQ-artige Fragen aus einer kuratierten Wissensdatenbank zu beantworten?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Speech synthesis",
                      "de": "Sprachsynthese"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Translator",
                      "de": "Translator"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Question answering",
                      "de": "Question Answering"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "Question answering returns answers from trusted question-answer pairs or documents.",
              "de": "Question Answering liefert Antworten aus vertrauenswürdigen Frage-Antwort-Paaren oder Dokumenten."
          },
          "hint": {
              "en": "Think about FAQ bots over known content.",
              "de": "Denken Sie an FAQ-Bots über bekannte Inhalte."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/overview"
      },
      {
          "id": "ai900-269",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which Azure AI capability converts spoken audio into written text?",
              "de": "Welche Azure-AI-Fähigkeit wandelt gesprochene Audiodaten in geschriebenen Text um?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Image analysis",
                      "de": "Bildanalyse"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Question answering",
                      "de": "Question Answering"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Translator",
                      "de": "Translator"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Speech to text",
                      "de": "Sprache-zu-Text"
                  }
              }
          ],
          "correct": [
              "D"
          ],
          "explanation": {
              "en": "Speech to text transcribes audio into text for captions, transcripts, and voice interfaces.",
              "de": "Sprache-zu-Text transkribiert Audio in Text für Untertitel, Transkripte und Sprachschnittstellen."
          },
          "hint": {
              "en": "Choose the feature that listens and transcribes.",
              "de": "Wählen Sie die Funktion, die zuhört und transkribiert."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/speech-service/overview"
      },
      {
          "id": "ai900-270",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which Azure AI service should you use to convert a German product description into English?",
              "de": "Welchen Azure-AI-Dienst sollten Sie verwenden, um eine deutsche Produktbeschreibung ins Englische zu übersetzen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Translator",
                      "de": "Translator"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Face service",
                      "de": "Face-Dienst"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Custom Vision",
                      "de": "Custom Vision"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Automated ML",
                      "de": "Automated ML"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Translator is the Azure AI service for machine translation between languages.",
              "de": "Translator ist der Azure-AI-Dienst für maschinelle Übersetzung zwischen Sprachen."
          },
          "hint": {
              "en": "Pick the service focused on language translation.",
              "de": "Wählen Sie den Dienst, der auf Sprachübersetzung ausgerichtet ist."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/translator/overview"
      },
      {
          "id": "ai900-271",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A contact center wants to monitor customer frustration trends in chat transcripts.",
              "de": "Ein Contact Center möchte Trends der Kundenfrustration in Chat-Transkripten überwachen."
          },
          "question": {
              "en": "Which Azure AI feature is best for identifying positive, neutral, or negative tone in customer chat logs?",
              "de": "Welche Azure-AI-Funktion eignet sich am besten, um positiven, neutralen oder negativen Ton in Kundenchatprotokollen zu erkennen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Sentiment analysis",
                      "de": "Sentimentanalyse"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Image classification",
                      "de": "Bildklassifizierung"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Sentiment analysis evaluates text to estimate emotional tone or opinion.",
              "de": "Die Sentimentanalyse wertet Text aus, um emotionalen Ton oder Meinungen einzuschätzen."
          },
          "hint": {
              "en": "The clue is positive versus negative language.",
              "de": "Der Hinweis ist positive gegenüber negativer Sprache."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview"
      },
      {
          "id": "ai900-272",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which Azure AI Language feature identifies people, organizations, and dates in a contract?",
              "de": "Welche Funktion von Azure AI Language identifiziert Personen, Organisationen und Daten in einem Vertrag?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Language detection",
                      "de": "Spracherkennung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Text summarization",
                      "de": "Textzusammenfassung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Named entity recognition",
                      "de": "Erkennung benannter Entitäten"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "Named entity recognition extracts structured entities such as names, places, dates, and organizations.",
              "de": "Die Erkennung benannter Entitäten extrahiert strukturierte Entitäten wie Namen, Orte, Daten und Organisationen."
          },
          "hint": {
              "en": "Look for the feature that spots specific real-world entities.",
              "de": "Suchen Sie nach der Funktion, die konkrete reale Entitäten erkennt."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview"
      },
      {
          "id": "ai900-273",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A support bot receives many short requests with different service intents.",
              "de": "Ein Support-Bot erhält viele kurze Anfragen mit unterschiedlichen Service-Absichten."
          },
          "question": {
              "en": "Which Azure AI service should a helpdesk bot use to understand whether a user wants to reset a password or track a shipment?",
              "de": "Welchen Azure-AI-Dienst sollte ein Helpdesk-Bot verwenden, um zu verstehen, ob ein Benutzer ein Passwort zurücksetzen oder eine Sendung verfolgen möchte?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Conversational Language Understanding",
                      "de": "Conversational Language Understanding"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Face service",
                      "de": "Face-Dienst"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Image analysis",
                      "de": "Bildanalyse"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "CLU is designed to map natural language utterances to intents such as password reset or shipment tracking.",
              "de": "CLU ist dafür ausgelegt, natürlichsprachliche Äußerungen Absichten wie Passwortzurücksetzung oder Sendungsverfolgung zuzuordnen."
          },
          "hint": {
              "en": "Think about intent classification in conversation.",
              "de": "Denken Sie an Intent-Klassifizierung in Gesprächen."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/overview"
      },
      {
          "id": "ai900-274",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which Azure AI capability reads written text aloud with a natural-sounding voice?",
              "de": "Welche Azure-AI-Fähigkeit liest geschriebenen Text mit einer natürlich klingenden Stimme vor?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Named entity recognition",
                      "de": "Erkennung benannter Entitäten"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Language detection",
                      "de": "Spracherkennung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Text to speech",
                      "de": "Text-zu-Sprache"
                  }
              }
          ],
          "correct": [
              "D"
          ],
          "explanation": {
              "en": "Text to speech synthesizes audio from text, which is useful for voice assistants and accessibility.",
              "de": "Text-zu-Sprache synthetisiert Audio aus Text und ist nützlich für Sprachassistenten und Barrierefreiheit."
          },
          "hint": {
              "en": "Choose the feature that speaks instead of listens.",
              "de": "Wählen Sie die Funktion, die spricht statt zuzuhören."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/speech-service/overview"
      },
      {
          "id": "ai900-275",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which Azure AI Language feature determines whether an email is written in French, German, or Spanish?",
              "de": "Welche Funktion von Azure AI Language bestimmt, ob eine E-Mail auf Französisch, Deutsch oder Spanisch verfasst ist?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Language detection",
                      "de": "Spracherkennung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Question answering",
                      "de": "Question Answering"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Speech synthesis",
                      "de": "Sprachsynthese"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Language detection identifies the language of the supplied text.",
              "de": "Die Spracherkennung identifiziert die Sprache des bereitgestellten Textes."
          },
          "hint": {
              "en": "The task is to identify the language itself.",
              "de": "Die Aufgabe besteht darin, die Sprache selbst zu identifizieren."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview"
      },
      {
          "id": "ai900-276",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A university wants to reduce repetitive email inquiries about deadlines and requirements.",
              "de": "Eine Hochschule möchte wiederholte E-Mail-Anfragen zu Fristen und Anforderungen reduzieren."
          },
          "question": {
              "en": "Which Azure AI capability is best for a university portal that answers admission questions from approved policy documents?",
              "de": "Welche Azure-AI-Fähigkeit eignet sich am besten für ein Hochschulportal, das Zulassungsfragen aus genehmigten Richtliniendokumenten beantwortet?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Custom Vision",
                      "de": "Custom Vision"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Question answering",
                      "de": "Question Answering"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Image classification",
                      "de": "Bildklassifizierung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Regression",
                      "de": "Regression"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Question answering is designed to return answers from curated knowledge sources.",
              "de": "Question Answering ist darauf ausgelegt, Antworten aus kuratierten Wissensquellen zurückzugeben."
          },
          "hint": {
              "en": "Think about FAQ-style answers from trusted documents.",
              "de": "Denken Sie an FAQ-ähnliche Antworten aus vertrauenswürdigen Dokumenten."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/language-service/question-answering/overview"
      },
      {
          "id": "ai900-277",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which Azure AI service can transcribe call-center audio in near real time?",
              "de": "Welcher Azure-AI-Dienst kann Callcenter-Audio nahezu in Echtzeit transkribieren?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Translator",
                      "de": "Translator"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Question answering",
                      "de": "Question Answering"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Speech service",
                      "de": "Sprachdienst"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Face service",
                      "de": "Face-Dienst"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "The Speech service provides speech recognition features, including real-time transcription.",
              "de": "Der Sprachdienst bietet Spracherkennungsfunktionen, einschließlich Echtzeittranskription."
          },
          "hint": {
              "en": "Pick the service for audio understanding.",
              "de": "Wählen Sie den Dienst für das Verstehen von Audio."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/speech-service/overview"
      },
      {
          "id": "ai900-278",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": null,
          "question": {
              "en": "Which Azure AI Language feature helps locate passport numbers or other sensitive personal details in text?",
              "de": "Welche Funktion von Azure AI Language hilft dabei, Passnummern oder andere sensible persönliche Angaben in Texten zu finden?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Image analysis",
                      "de": "Bildanalyse"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Text to speech",
                      "de": "Text-zu-Sprache"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "PII detection",
                      "de": "PII-Erkennung"
                  }
              }
          ],
          "correct": [
              "D"
          ],
          "explanation": {
              "en": "PII detection is used to identify sensitive personal information in text.",
              "de": "Die PII-Erkennung wird verwendet, um sensible persönliche Informationen in Texten zu identifizieren."
          },
          "hint": {
              "en": "Look for the feature focused on personal data in text.",
              "de": "Suchen Sie nach der Funktion, die sich auf persönliche Daten in Texten konzentriert."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview"
      },
      {
          "id": "ai900-279",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A travel marketplace wants renters and hosts to chat without sharing a common language.",
              "de": "Ein Reise-Marktplatz möchte, dass Mieter und Gastgeber chatten können, ohne eine gemeinsame Sprache zu sprechen."
          },
          "question": {
              "en": "Which Azure AI service is most appropriate for translating live chat messages between travelers and hosts?",
              "de": "Welcher Azure-AI-Dienst eignet sich am besten, um Live-Chat-Nachrichten zwischen Reisenden und Gastgebern zu übersetzen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Translator",
                      "de": "Translator"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Face service",
                      "de": "Face-Dienst"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Automated ML",
                      "de": "Automated ML"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Translator is built for converting text between languages and supports multilingual communication scenarios.",
              "de": "Translator ist für die Umwandlung von Text zwischen Sprachen ausgelegt und unterstützt mehrsprachige Kommunikationsszenarien."
          },
          "hint": {
              "en": "The task is direct text translation across languages.",
              "de": "Die Aufgabe ist die direkte Textübersetzung zwischen Sprachen."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/translator/overview"
      },
      {
          "id": "ai900-280",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which Azure AI Language feature can create a concise summary of long incident reports?",
              "de": "Welche Funktion von Azure AI Language kann eine knappe Zusammenfassung langer Störungsberichte erstellen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Text summarization",
                      "de": "Textzusammenfassung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Face detection",
                      "de": "Gesichtserkennung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Text summarization reduces longer text into the most important points.",
              "de": "Die Textzusammenfassung reduziert längere Texte auf die wichtigsten Punkte."
          },
          "hint": {
              "en": "Choose the feature that shortens text while preserving meaning.",
              "de": "Wählen Sie die Funktion, die Text verkürzt und dabei die Bedeutung bewahrt."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview"
      },
      {
          "id": "ai900-281",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which AI workload focuses on understanding and processing human language?",
              "de": "Welcher KI-Workload konzentriert sich auf das Verstehen und Verarbeiten menschlicher Sprache?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Computer vision",
                      "de": "Computer Vision"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Regression",
                      "de": "Regression"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Natural language processing",
                      "de": "Natural Language Processing"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Clustering",
                      "de": "Clustering"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "NLP deals with text and speech rather than pixels or numeric forecasting.",
              "de": "NLP befasst sich mit Text und Sprache statt mit Pixeln oder numerischen Prognosen."
          },
          "hint": {
              "en": "Pick the workload centered on language.",
              "de": "Wählen Sie den Workload, der sich auf Sprache konzentriert."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview"
      },
      {
          "id": "ai900-282",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A bank is modernizing its phone system with a voice-enabled self-service assistant.",
              "de": "Eine Bank modernisiert ihr Telefonsystem mit einem sprachgesteuerten Self-Service-Assistenten."
          },
          "question": {
              "en": "Which Azure AI service should you use when an IVR system must recognize caller speech and reply with synthesized voice?",
              "de": "Welchen Azure-AI-Dienst sollten Sie verwenden, wenn ein IVR-System die Sprache des Anrufers erkennen und mit synthetischer Stimme antworten muss?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Custom Vision",
                      "de": "Custom Vision"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Translator",
                      "de": "Translator"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Question answering",
                      "de": "Question Answering"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Speech service",
                      "de": "Sprachdienst"
                  }
              }
          ],
          "correct": [
              "D"
          ],
          "explanation": {
              "en": "The Speech service includes both speech recognition and speech synthesis capabilities.",
              "de": "Der Sprachdienst umfasst sowohl Spracherkennung als auch Sprachsynthese."
          },
          "hint": {
              "en": "The solution must both listen and speak.",
              "de": "Die Lösung muss sowohl zuhören als auch sprechen."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/speech-service/overview"
      },
      {
          "id": "ai900-283",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which Azure AI Language feature finds the main phrases in a product review?",
              "de": "Welche Funktion von Azure AI Language findet die wichtigsten Ausdrücke in einer Produktbewertung?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Key phrase extraction",
                      "de": "Schlüsselbegriffextraktion"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Image captioning",
                      "de": "Bildbeschreibung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Face analysis",
                      "de": "Gesichtsanalyse"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Key phrase extraction identifies important terms that summarize the content of the text.",
              "de": "Die Schlüsselbegriffextraktion identifiziert wichtige Begriffe, die den Inhalt des Textes zusammenfassen."
          },
          "hint": {
              "en": "Look for the feature that highlights the core terms.",
              "de": "Suchen Sie nach der Funktion, die die Kernbegriffe hervorhebt."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview"
      },
      {
          "id": "ai900-284",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": null,
          "question": {
              "en": "For a Conversational Language Understanding project, what must you provide during training?",
              "de": "Was müssen Sie während des Trainings für ein Conversational-Language-Understanding-Projekt bereitstellen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Only unlabeled images",
                      "de": "Nur ungelabelte Bilder"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Example utterances labeled with intents and entities",
                      "de": "Beispieläußerungen, die mit Intents und Entitäten gelabelt sind"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "A list of bounding boxes",
                      "de": "Eine Liste von Begrenzungsrahmen"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "A GPU driver package",
                      "de": "Ein GPU-Treiberpaket"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "CLU learns from sample utterances that are tagged with the intents and entities the model should recognize.",
              "de": "CLU lernt aus Beispieläußerungen, die mit den Intents und Entitäten markiert sind, die das Modell erkennen soll."
          },
          "hint": {
              "en": "Think about supervised training data for language intents.",
              "de": "Denken Sie an überwachte Trainingsdaten für Sprach-Intents."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/overview"
      },
      {
          "id": "ai900-285",
          "type": "single",
          "domain": {
              "en": "Describe Features of NLP on Azure",
              "de": "Features von NLP auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A media-monitoring platform needs to tag articles with the organizations and people mentioned.",
              "de": "Eine Medien-Monitoring-Plattform muss Artikel mit den erwähnten Organisationen und Personen kennzeichnen."
          },
          "question": {
              "en": "Which Azure AI Language feature should be used to extract company names, people, and locations from news articles?",
              "de": "Welche Funktion von Azure AI Language sollte verwendet werden, um Firmennamen, Personen und Orte aus Nachrichtenartikeln zu extrahieren?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Text to speech",
                      "de": "Text-zu-Sprache"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Language detection",
                      "de": "Spracherkennung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Named entity recognition",
                      "de": "Erkennung benannter Entitäten"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "Named entity recognition identifies structured entities within unstructured text.",
              "de": "Die Erkennung benannter Entitäten identifiziert strukturierte Entitäten in unstrukturiertem Text."
          },
          "hint": {
              "en": "The answer should find real-world names and places in text.",
              "de": "Die Antwort sollte reale Namen und Orte in Texten finden."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview"
      },
      {
          "id": "ai900-286",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which Azure service provides access to GPT and DALL-E models with Azure governance and security controls?",
              "de": "Welcher Azure-Dienst bietet Zugriff auf GPT- und DALL-E-Modelle mit Azure-Governance- und Sicherheitskontrollen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Azure OpenAI Service",
                      "de": "Azure OpenAI Service"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Azure AI Vision",
                      "de": "Azure AI Vision"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Azure Machine Learning designer only",
                      "de": "Nur Azure Machine Learning Designer"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Azure Face service",
                      "de": "Azure Face-Dienst"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Azure OpenAI Service hosts foundation models such as GPT and DALL-E within Azure's platform environment.",
              "de": "Azure OpenAI Service hostet Foundation-Modelle wie GPT und DALL-E innerhalb der Azure-Plattformumgebung."
          },
          "hint": {
              "en": "Choose the Azure service dedicated to generative foundation models.",
              "de": "Wählen Sie den Azure-Dienst, der generativen Foundation-Modellen gewidmet ist."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/openai/overview"
      },
      {
          "id": "ai900-287",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which model family is best known for generating natural-language responses and summaries?",
              "de": "Welche Modellfamilie ist am bekanntesten für die Erzeugung natürlichsprachlicher Antworten und Zusammenfassungen?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "ResNet",
                      "de": "ResNet"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "GPT",
                      "de": "GPT"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "XGBoost",
                      "de": "XGBoost"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "k-means",
                      "de": "k-means"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "GPT models are optimized for language generation tasks such as drafting, summarizing, and conversation.",
              "de": "GPT-Modelle sind für Sprachgenerierungsaufgaben wie Entwürfe, Zusammenfassungen und Konversation optimiert."
          },
          "hint": {
              "en": "Think of the model family behind many chat experiences.",
              "de": "Denken Sie an die Modellfamilie hinter vielen Chat-Erlebnissen."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models"
      },
      {
          "id": "ai900-288",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "What is DALL-E primarily used for?",
              "de": "Wofür wird DALL-E hauptsächlich verwendet?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Speech recognition",
                      "de": "Spracherkennung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Spreadsheet forecasting",
                      "de": "Tabellenkalkulationsprognosen"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Text-to-image generation",
                      "de": "Text-zu-Bild-Generierung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Database indexing",
                      "de": "Datenbankindizierung"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "DALL-E generates images from text prompts.",
              "de": "DALL-E erzeugt Bilder aus Textprompts."
          },
          "hint": {
              "en": "The output is images, not text labels.",
              "de": "Die Ausgabe sind Bilder und keine Textlabels."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models"
      },
      {
          "id": "ai900-289",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "What is prompt engineering?",
              "de": "Was ist Prompt Engineering?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Training a new neural network from scratch",
                      "de": "Das Trainieren eines neuen neuronalen Netzes von Grund auf"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Removing all guardrails from a model",
                      "de": "Das Entfernen aller Leitplanken aus einem Modell"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Converting speech into text",
                      "de": "Das Umwandeln von Sprache in Text"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Designing prompts to guide model behavior and output",
                      "de": "Das Gestalten von Prompts, um Modellverhalten und Ausgabe zu steuern"
                  }
              }
          ],
          "correct": [
              "D"
          ],
          "explanation": {
              "en": "Prompt engineering is the practice of structuring instructions and context so the model produces better results.",
              "de": "Prompt Engineering ist die Praxis, Anweisungen und Kontext so zu strukturieren, dass das Modell bessere Ergebnisse liefert."
          },
          "hint": {
              "en": "Think about improving outputs through better instructions.",
              "de": "Denken Sie daran, Ausgaben durch bessere Anweisungen zu verbessern."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/prompt-engineering"
      },
      {
          "id": "ai900-290",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "What is the main purpose of retrieval-augmented generation (RAG)?",
              "de": "Was ist der Hauptzweck von Retrieval-Augmented Generation (RAG)?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "To ground responses with retrieved external data",
                      "de": "Antworten mit abgerufenen externen Daten zu fundieren"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "To replace all prompts with images",
                      "de": "Alle Prompts durch Bilder zu ersetzen"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "To remove the need for any data source",
                      "de": "Die Notwendigkeit einer Datenquelle vollständig zu entfernen"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "To turn a classifier into a speech model",
                      "de": "Einen Klassifikator in ein Sprachmodell umzuwandeln"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "RAG retrieves relevant information and supplies it to the model so answers are better grounded.",
              "de": "RAG ruft relevante Informationen ab und liefert sie dem Modell, sodass Antworten besser fundiert sind."
          },
          "hint": {
              "en": "Look for the option about grounding with trusted data.",
              "de": "Suchen Sie nach der Option zur Fundierung mit vertrauenswürdigen Daten."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview"
      },
      {
          "id": "ai900-291",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A company wants employees to get policy answers with supporting source passages.",
              "de": "Ein Unternehmen möchte, dass Mitarbeiter Richtlinienantworten mit unterstützenden Quellpassagen erhalten."
          },
          "question": {
              "en": "Which approach is best when an HR chatbot must answer questions from internal policy documents and show where the answer came from?",
              "de": "Welcher Ansatz ist am besten geeignet, wenn ein HR-Chatbot Fragen aus internen Richtliniendokumenten beantworten und zeigen soll, woher die Antwort stammt?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Image classification",
                      "de": "Bildklassifizierung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "RAG",
                      "de": "RAG"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "OCR only",
                      "de": "Nur OCR"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "RAG combines retrieval from trusted documents with generation, which helps provide grounded answers and references.",
              "de": "RAG kombiniert das Abrufen vertrauenswürdiger Dokumente mit Generierung, was fundierte Antworten und Verweise unterstützt."
          },
          "hint": {
              "en": "The chatbot should use approved documents before generating a reply.",
              "de": "Der Chatbot sollte genehmigte Dokumente verwenden, bevor er eine Antwort generiert."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview"
      },
      {
          "id": "ai900-292",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "What is a key benefit of content filtering in a generative AI solution?",
              "de": "Was ist ein wesentlicher Vorteil der Inhaltsfilterung in einer generativen KI-Lösung?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "It guarantees the model is always factually correct",
                      "de": "Es garantiert, dass das Modell immer faktisch korrekt ist"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "It retrains the model after every prompt",
                      "de": "Es trainiert das Modell nach jedem Prompt neu"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "It can help detect and block harmful prompts or responses",
                      "de": "Es kann helfen, schädliche Prompts oder Antworten zu erkennen und zu blockieren"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "It turns text generation into OCR",
                      "de": "Es verwandelt Textgenerierung in OCR"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "Content filtering helps reduce unsafe or policy-violating interactions, though it is not the only safety measure needed.",
              "de": "Die Inhaltsfilterung hilft, unsichere oder richtlinienwidrige Interaktionen zu verringern, ist jedoch nicht die einzige erforderliche Sicherheitsmaßnahme."
          },
          "hint": {
              "en": "Choose the option about reducing unsafe content.",
              "de": "Wählen Sie die Option zur Verringerung unsicherer Inhalte."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/content-filter"
      },
      {
          "id": "ai900-293",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which setting most directly affects how random or creative a model's response may be?",
              "de": "Welche Einstellung beeinflusst am direktesten, wie zufällig oder kreativ die Antwort eines Modells sein kann?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Endpoint name",
                      "de": "Endpunktname"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Workspace region",
                      "de": "Arbeitsbereichsregion"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Resource group",
                      "de": "Ressourcengruppe"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Temperature",
                      "de": "Temperatur"
                  }
              }
          ],
          "correct": [
              "D"
          ],
          "explanation": {
              "en": "Temperature influences response variability. Lower values are usually more deterministic, while higher values can be more creative.",
              "de": "Die Temperatur beeinflusst die Variabilität von Antworten. Niedrigere Werte sind meist deterministischer, höhere Werte können kreativer sein."
          },
          "hint": {
              "en": "Think about the parameter linked to creativity.",
              "de": "Denken Sie an den Parameter, der mit Kreativität verbunden ist."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/openai/overview"
      },
      {
          "id": "ai900-294",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": {
              "en": "A sales team wants AI assistance directly inside the tools they already use every day.",
              "de": "Ein Vertriebsteam möchte KI-Unterstützung direkt in den Tools, die es täglich nutzt."
          },
          "question": {
              "en": "Which generative AI experience best matches an assistant that drafts emails and summarizes meetings inside Microsoft 365 apps?",
              "de": "Welches generative KI-Erlebnis passt am besten zu einem Assistenten, der E-Mails entwirft und Besprechungen in Microsoft-365-Apps zusammenfasst?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Copilot",
                      "de": "Copilot"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Clustering",
                      "de": "Clustering"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Face detection",
                      "de": "Gesichtserkennung"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "A copilot is an AI assistant embedded in the user's work context to help with tasks such as drafting and summarizing.",
              "de": "Ein Copilot ist ein KI-Assistent, der in den Arbeitskontext des Benutzers eingebettet ist, um bei Aufgaben wie Entwürfen und Zusammenfassungen zu helfen."
          },
          "hint": {
              "en": "Think about an AI assistant embedded in productivity tools.",
              "de": "Denken Sie an einen KI-Assistenten, der in Produktivitätstools eingebettet ist."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/openai/overview"
      },
      {
          "id": "ai900-295",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "What does hallucination mean in generative AI?",
              "de": "Was bedeutet Halluzination bei generativer KI?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "The model responds more quickly than expected",
                      "de": "Das Modell antwortet schneller als erwartet"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "The model generates content that sounds plausible but is incorrect",
                      "de": "Das Modell erzeugt Inhalte, die plausibel klingen, aber falsch sind"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "The model refuses all prompts",
                      "de": "Das Modell lehnt alle Prompts ab"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "The model only works with images",
                      "de": "Das Modell funktioniert nur mit Bildern"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "Hallucination refers to generated output that appears convincing but is not grounded in reality or source data.",
              "de": "Halluzination bezeichnet generierte Ausgaben, die überzeugend wirken, aber nicht in der Realität oder in Quelldaten verankert sind."
          },
          "hint": {
              "en": "Look for plausible-sounding but false output.",
              "de": "Suchen Sie nach plausibel klingenden, aber falschen Ausgaben."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/openai/overview"
      },
      {
          "id": "ai900-296",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A healthcare provider is piloting a patient-facing assistant and wants stronger safeguards.",
              "de": "Ein Gesundheitsdienstleister pilotiert einen patientenorientierten Assistenten und möchte stärkere Sicherheitsmechanismen."
          },
          "question": {
              "en": "Which capability should a healthcare chatbot use to help reduce unsafe or disallowed generated responses?",
              "de": "Welche Fähigkeit sollte ein Healthcare-Chatbot verwenden, um unsichere oder unzulässige generierte Antworten zu reduzieren?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "OCR",
                      "de": "OCR"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Content filtering",
                      "de": "Inhaltsfilterung"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Clustering",
                      "de": "Clustering"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "Content filtering is one of the tools used to screen prompts and outputs for unsafe categories.",
              "de": "Die Inhaltsfilterung ist eines der Werkzeuge, mit denen Prompts und Ausgaben auf unsichere Kategorien geprüft werden."
          },
          "hint": {
              "en": "Think about moderation and safety screening.",
              "de": "Denken Sie an Moderation und Sicherheitsprüfung."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/content-filter"
      },
      {
          "id": "ai900-297",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "In a chat completion request, what is the role of the system message?",
              "de": "Welche Rolle hat die Systemnachricht in einer Chat-Completion-Anfrage?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "It stores embeddings in a vector index",
                      "de": "Sie speichert Embeddings in einem Vektorindex"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "It translates the user's prompt into another language",
                      "de": "Sie übersetzt den Prompt des Benutzers in eine andere Sprache"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "It trains the model on new data",
                      "de": "Sie trainiert das Modell mit neuen Daten"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "It sets high-level behavior and instructions for the assistant",
                      "de": "Sie legt das übergeordnete Verhalten und die Anweisungen für den Assistenten fest"
                  }
              }
          ],
          "correct": [
              "D"
          ],
          "explanation": {
              "en": "The system message defines the assistant's role, style, and constraints for the conversation.",
              "de": "Die Systemnachricht definiert Rolle, Stil und Einschränkungen des Assistenten für die Unterhaltung."
          },
          "hint": {
              "en": "Think about the instructions that shape the assistant before the user speaks.",
              "de": "Denken Sie an die Anweisungen, die den Assistenten prägen, bevor der Benutzer spricht."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/prompt-engineering"
      },
      {
          "id": "ai900-298",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "What are embeddings commonly used for in generative AI solutions?",
              "de": "Wofür werden Embeddings in generativen KI-Lösungen häufig verwendet?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Semantic search and retrieval",
                      "de": "Semantische Suche und Retrieval"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Turning images into speech",
                      "de": "Bilder in Sprache umwandeln"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Running OCR on paper forms",
                      "de": "OCR auf Papierformularen ausführen"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Detecting hardware failures",
                      "de": "Hardwarefehler erkennen"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "Embeddings represent meaning in vector form, which is useful for similarity search and retrieval workflows.",
              "de": "Embeddings stellen Bedeutung in Vektorform dar und sind nützlich für Ähnlichkeitssuche und Retrieval-Workflows."
          },
          "hint": {
              "en": "Look for the option related to finding semantically similar content.",
              "de": "Suchen Sie nach der Option, die mit dem Finden semantisch ähnlicher Inhalte zusammenhängt."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview"
      },
      {
          "id": "ai900-299",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "An advertising team is brainstorming poster concepts before hiring a designer.",
              "de": "Ein Werbeteam sammelt Ideen für Poster, bevor ein Designer beauftragt wird."
          },
          "question": {
              "en": "Which model is most appropriate when a marketing team wants concept art generated from text prompts?",
              "de": "Welches Modell ist am geeignetsten, wenn ein Marketingteam Konzeptgrafiken aus Textprompts erzeugen möchte?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Speech model",
                      "de": "Sprachmodell"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "DALL-E",
                      "de": "DALL-E"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Regression model",
                      "de": "Regressionsmodell"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "CLU",
                      "de": "CLU"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "DALL-E is the model family intended for generating images from text descriptions.",
              "de": "DALL-E ist die Modellfamilie, die für die Erzeugung von Bildern aus Textbeschreibungen vorgesehen ist."
          },
          "hint": {
              "en": "The desired output is image content.",
              "de": "Die gewünschte Ausgabe ist Bildinhalt."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models"
      },
      {
          "id": "ai900-300",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "For high-impact business decisions, what is the best practice when using generative AI outputs?",
              "de": "Was ist bei geschäftskritischen Entscheidungen die beste Praxis beim Einsatz generativer KI-Ausgaben?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Accept every output without review",
                      "de": "Jede Ausgabe ohne Prüfung akzeptieren"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Increase temperature to maximum",
                      "de": "Die Temperatur auf das Maximum erhöhen"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Have a human review and validate the output",
                      "de": "Eine menschliche Überprüfung und Validierung der Ausgabe durchführen"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Remove all safety controls",
                      "de": "Alle Sicherheitskontrollen entfernen"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "Human review helps catch mistakes and manage risk in sensitive or high-impact use cases.",
              "de": "Menschliche Überprüfung hilft, Fehler zu erkennen und Risiken in sensiblen oder folgenreichen Anwendungsfällen zu steuern."
          },
          "hint": {
              "en": "Responsible use still needs human oversight.",
              "de": "Verantwortungsvoller Einsatz erfordert weiterhin menschliche Aufsicht."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/openai/overview"
      },
      {
          "id": "ai900-301",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "Which method can often improve generative AI results without retraining the model?",
              "de": "Welche Methode kann generative KI-Ergebnisse oft verbessern, ohne das Modell neu zu trainieren?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Deleting source documents",
                      "de": "Quelldokumente löschen"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Reducing every prompt to one word",
                      "de": "Jeden Prompt auf ein Wort reduzieren"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Disabling content filters",
                      "de": "Inhaltsfilter deaktivieren"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Prompt engineering",
                      "de": "Prompt Engineering"
                  }
              }
          ],
          "correct": [
              "D"
          ],
          "explanation": {
              "en": "Improving the instructions, examples, and context in prompts can significantly improve results without model retraining.",
              "de": "Die Verbesserung von Anweisungen, Beispielen und Kontext in Prompts kann Ergebnisse deutlich verbessern, ohne das Modell neu zu trainieren."
          },
          "hint": {
              "en": "Think about changing the input rather than the model.",
              "de": "Denken Sie daran, die Eingabe statt des Modells zu ändern."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/prompt-engineering"
      },
      {
          "id": "ai900-302",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A product support bot must answer only from the latest official manuals.",
              "de": "Ein Produktsupport-Bot darf nur auf Grundlage der neuesten offiziellen Handbücher antworten."
          },
          "question": {
              "en": "Which approach should a support assistant use when it must search product manuals before composing an answer?",
              "de": "Welchen Ansatz sollte ein Support-Assistent verwenden, wenn er vor der Formulierung einer Antwort Produkthandbücher durchsuchen muss?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "RAG",
                      "de": "RAG"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Object detection",
                      "de": "Objekterkennung"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "OCR only",
                      "de": "Nur OCR"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Face detection",
                      "de": "Gesichtserkennung"
                  }
              }
          ],
          "correct": [
              "A"
          ],
          "explanation": {
              "en": "RAG retrieves relevant manual content first and then uses it to ground the generated answer.",
              "de": "RAG ruft zunächst relevante Inhalte aus Handbüchern ab und nutzt sie dann, um die generierte Antwort zu fundieren."
          },
          "hint": {
              "en": "Search first, then generate.",
              "de": "Zuerst suchen, dann generieren."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview"
      },
      {
          "id": "ai900-303",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "easy",
          "scenario": null,
          "question": {
              "en": "What best describes a copilot in the context of generative AI?",
              "de": "Was beschreibt einen Copiloten im Kontext generativer KI am besten?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "A standalone OCR scanner",
                      "de": "Ein eigenständiger OCR-Scanner"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "An AI assistant grounded in user and task context to help complete work",
                      "de": "Ein KI-Assistent, der im Benutzer- und Aufgabenkontext verankert ist, um Arbeit zu unterstützen"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "A clustering algorithm for unlabeled data",
                      "de": "Ein Clustering-Algorithmus für unmarkierte Daten"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "A tool used only for image compression",
                      "de": "Ein Werkzeug, das nur für Bildkomprimierung verwendet wird"
                  }
              }
          ],
          "correct": [
              "B"
          ],
          "explanation": {
              "en": "A copilot is an AI assistant that helps users within a specific workflow or application context.",
              "de": "Ein Copilot ist ein KI-Assistent, der Benutzern innerhalb eines bestimmten Arbeitsablaufs oder Anwendungskontexts hilft."
          },
          "hint": {
              "en": "Think about AI assistance embedded in real tasks.",
              "de": "Denken Sie an KI-Unterstützung, die in reale Aufgaben eingebettet ist."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/openai/overview"
      },
      {
          "id": "ai900-304",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": null,
          "question": {
              "en": "Why might an enterprise choose Azure OpenAI Service for a business application instead of using a public consumer chatbot?",
              "de": "Warum könnte sich ein Unternehmen für Azure OpenAI Service in einer Geschäftsanwendung entscheiden, anstatt einen öffentlichen Consumer-Chatbot zu verwenden?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Because it removes all need for security controls",
                      "de": "Weil dadurch alle Sicherheitskontrollen überflüssig werden"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Because it only works without Azure",
                      "de": "Weil es nur ohne Azure funktioniert"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Because it offers Azure-based security, governance, and enterprise integration",
                      "de": "Weil es Azure-basierte Sicherheit, Governance und Unternehmensintegration bietet"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Because it cannot use GPT models",
                      "de": "Weil es keine GPT-Modelle verwenden kann"
                  }
              }
          ],
          "correct": [
              "C"
          ],
          "explanation": {
              "en": "Azure OpenAI Service is attractive for enterprise scenarios because it fits Azure's security, compliance, and management model.",
              "de": "Azure OpenAI Service ist für Unternehmensszenarien attraktiv, weil es in das Azure-Sicherheits-, Compliance- und Verwaltungsmodell passt."
          },
          "hint": {
              "en": "Focus on enterprise controls and platform integration.",
              "de": "Konzentrieren Sie sich auf Unternehmenskontrollen und Plattformintegration."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/openai/overview"
      },
      {
          "id": "ai900-305",
          "type": "single",
          "domain": {
              "en": "Describe Features of Generative AI on Azure",
              "de": "Features generativer KI auf Azure beschreiben"
          },
          "difficulty": "medium",
          "scenario": {
              "en": "A team notices that similar user questions often receive answers in different styles and levels of detail.",
              "de": "Ein Team bemerkt, dass ähnliche Benutzerfragen oft Antworten in unterschiedlichen Stilen und Detaillierungsgraden erhalten."
          },
          "question": {
              "en": "What is the best first step if a generative AI app produces inconsistent answers because user prompts are vague?",
              "de": "Was ist der beste erste Schritt, wenn eine generative KI-App inkonsistente Antworten liefert, weil Benutzerprompts zu vage sind?"
          },
          "options": [
              {
                  "id": "A",
                  "text": {
                      "en": "Remove the system message",
                      "de": "Die Systemnachricht entfernen"
                  }
              },
              {
                  "id": "B",
                  "text": {
                      "en": "Disable content filtering",
                      "de": "Die Inhaltsfilterung deaktivieren"
                  }
              },
              {
                  "id": "C",
                  "text": {
                      "en": "Switch every request to image generation",
                      "de": "Jede Anfrage auf Bildgenerierung umstellen"
                  }
              },
              {
                  "id": "D",
                  "text": {
                      "en": "Add clearer role, context, constraints, and examples to the prompt",
                      "de": "Dem Prompt klarere Rolle, Kontext, Einschränkungen und Beispiele hinzufügen"
                  }
              }
          ],
          "correct": [
              "D"
          ],
          "explanation": {
              "en": "Clearer prompts usually improve consistency by telling the model exactly how it should behave and what output is expected.",
              "de": "Klarere Prompts verbessern in der Regel die Konsistenz, indem sie dem Modell genau sagen, wie es sich verhalten und welches Ergebnis erwartet wird."
          },
          "hint": {
              "en": "Think prompt quality before changing the whole solution.",
              "de": "Denken Sie zuerst an die Qualität des Prompts, bevor Sie die ganze Lösung ändern."
          },
          "reference": "https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/prompt-engineering"
      }
  ];
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