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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Certified: AI Transformation Leader |
| Exam Number: | AB-731 |
| Real Exam Qty: | 40-60 |
| Exam Format: | Build list, Drag and drop, Case studies, Multiple choice |
| Related Certifications: | Microsoft Certified: AI Transformation Leader |
| Passing Score: | 700 / 1000 |
| Exam Price: | USD 99 |
| Available Languages: | English |
| Exam Duration: | 45 minutes |
| Certificate Validity Period: | 1 year (renewable) |
| Sample Questions: | Microsoft AB-731 Sample Questions |
| Exam Way: | Online or at a test centre |
| Pre Condition: | No formal prerequisites. Recommended: familiarity with Microsoft 365 services, Azure AI services, and experience with adoption or change management in a business context. This certification is designed for business decision-makers at all levels; no coding is required. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ab-731 |
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NEW QUESTION # 21
A marketing team wants to automatically create product descriptions and campaign email drafts.
Which generative AI capability best meets this business need?
Answer: A
NEW QUESTION # 22
- For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Answer Area
* Content filtering controls can prevent AI-generated responses from exposing confidential and sensitive information. Answer: Yes
* AI-generated content can unintentionally reveal sensitive information if the generative AI model has access to unsecured data sources. Answer: Yes
* To prevent data exposure, only the prompts used by users must be protected by using policies. Answer:
No
* Yes - Content filtering (and related safety controls) can help reduce the chance that responses contain policy-violating or sensitive outputs by detecting and blocking certain categories of content. While filtering is not a perfect guarantee, it is a recognized control to prevent or reduce exposure risk in outputs (for example, blocking regulated data patterns, disallowed content categories, or unsafe disclosures).
* Yes - If a model (or the solution's retrieval layer) can access poorly governed repositories-such as broadly shared folders, misconfigured SharePoint sites, or unsecured databases-then the system can surface sensitive information in responses even without malicious intent. This is why access control, data classification, and permission hygiene are critical prerequisites for deploying AI assistants grounded in organizational content.
* No - Protecting only user prompts is insufficient. Data exposure can occur through multiple paths:
retrieved documents, generated outputs, logs/telemetry, training/fine-tuning data, and connector/index configuration. Preventing exposure requires layered controls: data governance (labels, DLP, least privilege), secure connectors, output filtering, auditing, and user training-not prompt policy alone.
NEW QUESTION # 23
You have a business unit that uses an AI solution to process loan applications.
You discover that the solution rejects the application of all applicants that are older than 60 years of age.
Which Microsoft responsible AI principle is this violating?
Answer: A
Explanation:
An AI solution that rejects all loan applications based solely on an applicant being older than a certain age violates the Fairness principle in Microsoft's Responsible AI framework.
Here is a breakdown of why this constitutes a violation:
Violation of the "Fairness" Principle
Microsoft defines fairness as the commitment to treating all individuals and groups equitably, avoiding biases that could lead to unfair discrimination.
Discriminatory Outcome: Automatically rejecting a group based on a protected characteristic (age) is a textbook example of algorithmic bias and "allocation harm," where an AI system unfairly denies resources or opportunities.
Lack of Individual Assessment: The system treats every applicant in a certain age group the same, ignoring individual creditworthiness, financial stability, or income.
Reference:
https://infusedinnovations.com/blog/responsible-ai-fairness
NEW QUESTION # 24
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: Batch API
An organization that runs continuous, large-scale workloads with Azure OpenAI models should choose the _______ pricing model.
An organization that runs continuous, large-scale, non-time-sensitive workloads with Azure OpenAI models should choose the Batch API pricing model.
This approach provides significant cost savings and operational advantages for high-volume, asynchronous tasks.
For continuous, non-urgent workloads, the Batch API provides the best balance of cost- effectiveness and throughput, often allowing organizations to cut their AI inference costs in half while maximizing scalability.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/batch?view=foundry-classic
NEW QUESTION # 25
- Select the answer that correctly completes the sentence.
To ensure that your organization follows trustworthy AI principles, the organization should establish an AI governance council to __________.
Answer:
Explanation:
Explanation:
guide AI strategy, ensure responsible AI oversight, and promote alignment across business units.
A trustworthy AI program requires more than technical implementation; it requires enterprise governance:
clear ownership, risk management, policy, and cross-functional coordination. An AI governance council's primary role is to provide strategic direction and oversight so AI initiatives align with business goals while meeting Responsible/Trustworthy AI expectations (fairness, reliability and safety, privacy and security, transparency, accountability, and inclusiveness).
Therefore, the best completion is that the council should guide AI strategy, ensure responsible AI oversight, and promote alignment across business units. This captures why councils are created: to avoid siloed deployments, define guardrails and approval processes, standardize evaluation/monitoring practices, and coordinate stakeholders such as legal, compliance, security, data governance, HR, and business leadership.
The council also helps prioritize use cases, establish policies for data use and access, set documentation requirements, and require ongoing monitoring and incident response for AI systems in production.
The other options are narrower operational responsibilities. Configuring and deploying models in Azure is typically owned by engineering/cloud teams. Day-to-day model training and labeling is handled by data science/ML teams. The council sits above those activities to ensure the organization's AI work is consistent, accountable, and strategically aligned.
NEW QUESTION # 26
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