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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Exam AB-731: AI Transformation Leader |
| Exam Number: | AB-731 |
| Exam Duration: | 45โ65 |
| Available Languages: | Japanese, English, French, German, Spanish, Chinese (Simplified) |
| Exam Format: | Multiple response, Yes/No, Case studies, Multiple choice, Drag and drop |
| Real Exam Qty: | 40โ60 |
| Passing Score: | 700 |
| Certificate Validity Period: | 12 months |
| Exam Price: | $99 USD |
| 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 |
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NEW QUESTION # 50
Your company plans to use generative AI to help summarize and analyze internal business documents. You need to recommend a solution to prevent generative AI from accessing confidential or classified information.
What should you include in the recommendation?
Answer: B
Explanation:
Preventing generative AI from accessing confidential or classified information is primarily a data access and classification control problem. The most effective broad solution is data governance (B) -the framework of policies and controls that ensures sensitive content is identified, classified, protected, and access is restricted using least privilege. Data governance includes information classification/sensitivity labels, access control reviews, secure sharing practices, data loss prevention (DLP), and auditing-controls that directly limit what data is available to the AI through permission trimming and policy enforcement.
An information barrier (A) policy is more specific: it's intended to prevent communication and collaboration between defined user groups (often for regulatory/ethical walls). It does not comprehensively address document classification or restrict AI access to sensitive files across the tenant. A data retention policy (C) governs how long content is kept and when it's deleted; it's not a primary access-prevention mechanism. Communication monitoring (D) is a detection/oversight control; it can help identify risky activity, but it does not itself prevent access to confidential content by the AI.
Therefore, to prevent AI from surfacing confidential/classified data during summarization and analysis, you should recommend data governance as the primary control layer.
NEW QUESTION # 51
DRAG DROP - Match the Microsoft responsible AI principles to the appropriate descriptions. NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
Answer Area
* Protect personal information and apply strong safeguards to keep data secure. Answer: Privacy and security
* Make AI solutions understandable by explaining how and why decisions are made. Answer:
Transparency
* Design AI solutions that are accessible to people of all abilities and experiences. Answer: Inclusiveness
* Involve human oversight in the control of AI solutions. Answer: Accountability Microsoft's Responsible AI principles provide a practical framework for building and deploying trustworthy AI systems.
Privacy and security is the principle focused on protecting personal information and ensuring strong safeguards-such as least privilege access, encryption, DLP controls, secure storage, and auditing-so data remains protected throughout collection, processing, and usage.
Transparency is about making AI systems understandable. This includes communicating when AI is being used, explaining system capabilities and limitations, and providing clarity on how outputs are produced (for example, what data sources are used, what assumptions apply, and why certain recommendations are generated). Transparency reduces confusion and helps users calibrate trust appropriately.
Inclusiveness ensures AI solutions are designed to benefit people of diverse abilities, backgrounds, and experiences. Practically, this includes accessibility (assistive technologies, readable outputs), inclusive UX design, and testing across diverse user groups so the solution does not exclude or disadvantage certain populations.
Accountability emphasizes that humans and organizations remain responsible for AI outcomes. Involving human oversight is a direct expression of accountability: setting governance, defining who owns decisions, establishing escalation paths, and ensuring there are controls for review, intervention, and correction when AI outputs are unsafe or incorrect.
NEW QUESTION # 52
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Answer:
Explanation:
Explanation:
The correct selections are Yes, No, Yes. Prompt engineering is the practice of designing clear and effective inputs that guide a generative AI model toward relevant, accurate, and useful output. Guidelines, constraints, role instructions, tone requirements, and output-format rules are all common prompt-engineering techniques, so statement A is true. Statement B is false because examples are normally considered examples or few-shot demonstrations, not the instruction itself. The instruction tells the model what task to perform, while examples demonstrate the preferred pattern or output style. Statement C is true because context supplies the background information, source material, business scenario, audience, or constraints the model should use when generating the answer. Strong prompts usually combine instruction, context, constraints, and examples.
NEW QUESTION # 53
Your company uses a non-reasoning generative AI model to create textual content. You discover that the model's responses are inconsistent and do NOT meet expectations. You need to improve the prompts. What should you do? More than one answer choice may achieve the goal. Select the BEST answer.
Answer: A,B
Explanation:
When a non-reasoning generative AI model produces inconsistent outputs, the most reliable improvement is to make the prompt more specific, constrained, and demonstrative of what "good" looks like.
A is correct because adding high-quality examples is a form of few-shot prompting. Examples act like
"training wheels" at inference time: they show the model the desired structure, tone, level of detail, formatting rules, and boundaries. This reduces ambiguity and variance, especially for tasks like marketing copy, summaries, policy text, or customer replies. The more your examples resemble real target outputs (including edge cases), the more consistent the model's completions become.
B is correct because adding context, relevant source material, and explicit expectations narrows the model's degrees of freedom. Including the intended audience, purpose, constraints (length, voice, banned claims), and trusted reference content (approved facts, product specs, policy excerpts) helps the model stay aligned and reduces hallucinations and off-brand language. This is also where you specify acceptance criteria such as
"must include 3 bullet points," "use UK English," or "cite only provided text." C is not best: technical jargon can confuse or bias output if it's not aligned to the task; clarity beats jargon. D is not best: a single concise requirement is usually under-specified and often increases variability.
NEW QUESTION # 54
Your company is developing an AI-powered customer support agent.
You need to ensure that the solution follows Microsoft responsible AI principles.
Which two actions should you perform? Select the two BEST answers. Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,D
Explanation:
[B]
Under Microsoft's Responsible AI framework, this testing specifically addresses the principles of Fairness and Inclusiveness. To operationalize these, you should focus on the following testing and development areas:
Key Testing Focus Areas
*-> Cultural Sensitivity: Ensure the agent respects different values, perspectives, and international contexts to avoid causing offense or misunderstanding.
*-> Fairness and Bias Mitigation: Test the agent to ensure it treats all users equitably and does not reinforce societal stereotypes or discriminate based on protected characteristics like race, gender, or religion.
Accessibility: Validate that the agent is usable by people of all abilities, providing equal power and engagement regardless of their background.
Global Community Engagement: Involve diverse users and underserved communities in the pre- deployment validation and feedback process to identify representation gaps.
[E]
To align with Microsoft's Responsible AI principles, providing a clear disclaimer that users are interacting with an AI solution is a core requirement of the Transparency principle.
Under this principle and the Microsoft Responsible AI Standard, developers must ensure that AI systems are understandable and that users are not deceived into believing they are interacting with a human.
Reference:
https://learn.microsoft.com/en-us/legal/ai-code-of-conduct
https://www.microsoft.com/en-us/ai/principles-and-approach
NEW QUESTION # 55
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