Microsoft AB-731赤本合格率、AB-731 PDF
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Microsoft AB-731 認定試験の出題範囲：
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出題範囲
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AB-731 PDF、AB-731試験資料
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Microsoft AI Transformation Leader 認定 AB-731 試験問題 (Q58-Q63):
質問 # 58
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Answer Area
* Allowing AI models to make autonomous decisions supports the Microsoft responsible AI principle of accountability. Answer: No
* Regularly testing AI models for fairness and inclusiveness helps ensure they align with Microsoft's Responsible AI principles. Answer: Yes
* Protecting user data and limiting access to personal information supports the Microsoft responsible AI principles of privacy and security. Answer: Yes Microsoft's Responsible AI principles emphasize that people and organizations must remain accountable for AI systems and their outcomes. Accountability is strengthened by governance, human oversight, clear ownership, auditability, and processes to review and address issues-not by letting models make unchecked autonomous decisions. Therefore, statement 1 is No : increasing autonomy can actually increase risk unless paired with human-in-the-loop controls and clear escalation paths, because accountability requires clear responsibility for decisions and impacts.
Statement 2 is Yes because fairness and inclusiveness are explicitly supported through ongoing evaluation.
Regular testing helps detect disparate impact, performance gaps across user groups, and unintended bias introduced by data drift or changes in usage patterns. It's not a one-time activity; it's continuous assurance that the system behaves appropriately as conditions change.
Statement 3 is Yes because privacy and security are directly supported by protecting personal/sensitive data, enforcing least privilege access, and implementing controls such as data loss prevention, encryption, access logging, and strong identity governance. Limiting access to personal information reduces exposure and supports compliance obligations while aligning with privacy-by-design and secure-by-design expectations for AI-enabled solutions.
質問 # 59
Which business requirement most closely relates to grounding a generative AI model?
正解：D
解説：
Grounding in generative AI means ensuring model outputs are based on trusted, relevant information sources rather than only on the model's general training data. In a business context, grounding is about aligning responses with verified enterprise knowledge (policies, product documentation, internal procedures, approved FAQs, etc.) so the system is more accurate, consistent, and defensible. That is exactly what option D describes: "ensuring that verified company data sources are used for response generation." In Microsoft AI solution patterns, grounding is commonly achieved using retrieval-augmented generation (RAG). With RAG, the system retrieves relevant passages from approved company repositories (for example, indexed documents or knowledge bases) and supplies them as context to the model during response generation. This reduces hallucinations, improves factual correctness, and makes answers more relevant to the organization's reality-critical when AI is used for customer support, employee helpdesks, compliance guidance, or executive reporting.
The other options do not directly address grounding. A relates to localization/multilingual capability, B is a usage/telemetry metric, and C is an interaction method (natural language interface). They can all be important requirements, but none of them ensure outputs are anchored to verified company data-the core purpose of grounding.
質問 # 60
Your company plans to implement a proof of concept PoC agent that uses Azure OpenAI. The solution must start small and provide flexibility to scale usage as demand grows. Which pricing model should you use?
正解：B
解説：
For a proof of concept , the key requirements are low commitment , quick start , and the ability to scale up or down as you learn what real usage looks like. Azure OpenAI Standard On-Demand pricing is designed for exactly that: you pay per token consumed (input and output) on a pay-as-you-go basis, which makes it ideal when demand is uncertain or variable-typical in early pilots and PoCs.
By contrast, Provisioned (PTUs) is best when you have well-defined, predictable throughput and latency requirements -usually a more mature, production workload. PTUs involve reserving model processing capacity to achieve consistent performance and more predictable costs, which is usually premature for a PoC where actual traffic patterns are not yet known.
Batch API is optimized for asynchronous high-volume jobs with a target turnaround (for example, up to 24 hours) and discounted pricing. That's great for offline processing, but it does not match an interactive "agent" PoC that typically needs near-real-time responses and iterative testing.
Microsoft 365 Copilot is a separate SaaS licensing model and is not the Azure OpenAI pricing model for building your own agent solution.
質問 # 61
Your company plans to use generative AI to help build a website that will showcase various existing products.
Which capability best describes a benefit of using generative AI for this project? Select the BEST answer.
正解：B
解説：
For a product showcase website, the highest-impact, most directly relevant generative AI benefit is content creation at scale -producing consistent, high-quality product copy quickly. Option D matches a core generative AI capability: turning structured inputs (specifications such as dimensions, materials, features, compatibility, and use cases) into natural-language descriptions that are readable, persuasive, and formatted for web publishing. This accelerates catalog onboarding, reduces manual writing effort, and helps maintain a consistent tone and structure across thousands of SKUs.
Option A (translation) is also something generative AI can do, but it is a narrower, secondary capability compared to the primary website need: generating product copy from specs. Option B is predictive/analytical AI rather than generative content creation. Option C is plausible in ideation, but the scenario is about showcasing existing products, not inventing new ones. Therefore, D is the best answer because it aligns directly with the project's core workflow (product specs # publishable descriptions) and delivers measurable business value through speed, consistency, and reduced content production costs.
質問 # 62
You have a historical dataset that contains 1,000 records.
You need an AI solution that can analyze the data to identify patterns and predict future outcomes.
What should you include in the solution?
正解：A
解説：
The primary Microsoft AI solution designed to analyze large, historical datasets (thousands of records), identify complex patterns, and predict future outcomes is Azure Machine Learning, specifically utilizing Automated Machine Learning (AutoML).
Reference:
https://vslive.com/blogs/mshq-news-and-events/2025/04/azure-ml.aspx
質問 # 63
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