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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Exam AB-731: AI Transformation Leader |
| Exam Number: | AB-731 |
| Exam Duration: | 45–65 |
| Exam Format: | Drag and drop, Yes/No, Case studies, Multiple choice, Multiple response |
| Real Exam Qty: | 40–60 |
| Passing Score: | 700 |
| Exam Price: | $99 USD |
| Certificate Validity Period: | 12 months |
| Available Languages: | Spanish, Chinese (Simplified), Japanese, English, French, German |
| Recommended Training: | AB-731T00: AI Transformation Leader |
| Exam Registration: | Microsoft Certification Registration |
| Sample Questions: | Microsoft AB-731 Sample Questions |
| Exam Way: | Online proctored or onsite at authorized test centers |
| Pre Condition: | No mandatory prerequisites; recommended experience in business transformation, change management, and familiarity with Microsoft 365 and Azure AI services |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ab-731 |
>> AB-731 Fragen Und Antworten <<
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| Thema | Einzelheiten |
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90. Frage
Hotspot Question
Select the answer that correctly completes the sentence.
Antwort:
Begründung:
91. Frage
Which business requirement most closely relates to grounding a generative AI model?
Antwort: B
Begründung:
Ensuring that verified company data sources are used for response generation relates to grounding a generative AI model by anchoring its outputs in trusted, domain-specific, or enterprise-specific information. This process bridges the gap between the general knowledge a model has from its training data and the specific, up-to-date facts required for accurate, trustworthy business applications.
Reference:
https://decagon.ai/glossary/what-is-ai-grounding
92. Frage
- For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Antwort:
Begründung:
Explanation:
Answer Area
* Using incomplete or poor-quality data during generative AI model training can increase costs. Answer:
Yes
* AI models rely on training data to learn patterns and identify relationships to produce outputs. Answer:
Yes
* Generative AI models trained on non-representative datasets can produce inaccurate or unbalanced results. Answer: Yes
* Yes - Poor-quality or incomplete training data increases cost because it drives more iterations:
additional data cleaning, relabeling, re-training, and re-evaluation to reach acceptable performance. It can also increase operational costs after deployment if the model produces low-quality outputs that require human rework, escalations, or incident handling. In practice, data quality debt becomes model cost debt.
* Yes - Training data is the primary mechanism by which AI models learn statistical patterns and relationships. For generative models, the training corpus shapes language fluency, factual associations, style tendencies, and the kinds of content the model can produce. Without sufficient and appropriate training signals, outputs degrade.
* Yes - If the training dataset is not representative of the real-world population or business context, the model can systematically underperform for certain groups, topics, or edge cases. This can manifest as biased language, missing perspectives, and uneven accuracy, producing "unbalanced" results. That is why Responsible AI practice emphasizes representative data, evaluation across slices, and continuous monitoring.
93. Frage
Select the answer that correctly completes the sentence.
Prompt engineering is the process of __________.
Antwort:
Begründung:
Explanation:
crafting clear instructions to guide generative AI solutions in generating context-appropriate content.
Prompt engineering is fundamentally about how you communicate intent to a generative AI model so it produces outputs that meet business expectations. The best completion is "crafting clear instructions to guide generative AI solutions in generating context-appropriate content" because it captures the practical, day-to- day discipline: shaping the input (prompt) with the right task framing, constraints, context, and output format.
In real deployments, prompt engineering includes specifying the role and objective (for example, "act as a customer support agent"), providing the necessary context (product details, policy excerpts, audience), adding explicit requirements (tone, length, must/must-not statements), and defining structured output (JSON fields, bullet sections, headings). It can also include adding examples (few-shot prompting), clarifying what to do when information is missing, and instructing the model to cite only provided sources or to ask follow-up questions. These techniques reduce ambiguity, improve consistency, and lower the risk of hallucinations or off-brand responses.
The other options are not accurate definitions. "Integrating AI-powered tools into business workflows" describes solution adoption/integration, not prompt engineering. "Identifying and fixing errors in AI- generated content" is review/editing or quality assurance. "Designing, developing, and training generative AI models" is model development/ML engineering. Prompt engineering operates without changing model weights ; it's about steering model behavior through well-constructed instructions and context.
94. Frage
Select the answer that correctly completes the sentence.
Antwort:
Begründung:
Explanation:
Microsoft 365 Copilot connectors are used to bring external line-of-business and third-party content into the Microsoft 365 ecosystem so Copilot can use that information in supported experiences. Connectors help make data from systems outside Microsoft 365 discoverable and usable through Microsoft Graph-based indexing, while still respecting permissions and governance controls. This allows users to ask natural-language questions and receive answers grounded in business data from connected systems. Image and video analysis is associated with vision services, not Copilot connectors. Security incident summaries are aligned with Microsoft Security Copilot. Suggested email replies are a Microsoft 365 Copilot productivity feature in Outlook, not the primary purpose of connectors.
95. Frage
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