[2026年08月]更新のAB-731試験問題と有効なAB-731問題集PDF
AB-731ブレーン問題集学習ガイドにはヒントとコツで試験合格を目指そう
Microsoft AB-731 認定試験の出題範囲：
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質問 # 66
Your company is reviewing a new AI solution before deploying it. The company wants to ensure that the solution follows Microsoft responsible AI principles.
What is the best approach to achieve the goal? More than one answer choice may achieve the goal. Select the BEST answer.
- A. Test the AI solution to identify and mitigate potential unfair or inconsistent outcomes in its outputs.
- B. Prioritize model performance when tuning the AI solution.
- C. Enable the AI solution to collect and store personal data.
- D. Design the AI solution to automatically approve or reject customer loan applications.
正解：A
解説：
The correct answer is B. Testing the AI solution to identify and mitigate unfair or inconsistent outcomes directly supports Microsoft responsible AI principles, especially fairness, reliability and safety, accountability, and inclusiveness. Before deployment, organizations should evaluate whether the system behaves consistently across different user groups, avoids discriminatory outcomes, and produces safe and reliable outputs. Automatically approving or rejecting loan applications increases risk unless strong governance and human oversight are in place. Collecting personal data can increase privacy exposure if it is not necessary and controlled. Prioritizing model performance alone is also insufficient because a technically accurate model can still produce biased, unsafe, or non-transparent outcomes. Responsible AI requires evaluation, mitigation, oversight, and monitoring.
質問 # 67
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
* Microsoft 365 Copilot connectors enable you to index data from multiple sources to make the data available in Copilot. Answer: Yes
* You can build a custom Microsoft 365 Copilot connector when the available connectors do NOT meet your data integration requirements. Answer: Yes
* To use Microsoft 365 Copilot connectors, you need a Microsoft Copilot Studio license. Answer: No
* Yes - Microsoft 365 Copilot connectors (including synced connectors) are designed to bring external data into Microsoft Graph so it can be semantically indexed and surfaced in Microsoft 365 Copilot experiences. Microsoft explicitly states that synced connectors ingest/crawl content into Microsoft Graph where it's indexed and then available for Copilot prompts and citations.
* Yes - When Microsoft-provided connectors don't meet integration needs, organizations can create custom connectors (often referred to as Microsoft Graph connectors / custom connector development) to connect other data sources. This is a common extensibility path to index line-of-business repositories and make that content discoverable via Copilot and Microsoft Search.
* No - Using Microsoft 365 Copilot connectors does not require a Copilot Studio license. Connectors are generally configured and managed in Microsoft 365 admin/search experiences, and Microsoft's licensing guidance indicates that users can view connector data in Microsoft 365 Copilot and Microsoft Search with valid Microsoft 365/Office 365 licensing-Copilot Studio licensing is about building agents in Copilot Studio, not a prerequisite to use connectors.
質問 # 68
During AI adoption planning, leadership evaluates workforce readiness, operating models, and governance structures required to support AI at scale. Why is this step critical?
- A. It limits AI usage to technical teams only
- B. It replaces the need for AI infrastructure investments
- C. It eliminates the need for responsible AI reviews
- D. It ensures organizational readiness and sustainable AI adoption
正解：D
解説：
Assessing workforce skills, governance, and operating models ensures the organization can adopt AI responsibly and scale usage effectively.
Reference:
https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/scenarios/ai/plan
質問 # 69
Your company uses a generative AI solution.
You need to improve the quality of responses by using grounding.
Which statement accurately describes how grounding improves accuracy and relevancy?
- A. specifies the strengths and weaknesses of the AI model
- B. anchors the responses in specific data sources
- C. references a diverse set of people, disciplines, and perspectives
- D. explains how and why AI models generate content
正解：B
質問 # 70
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
* A manufacturer can use Azure Vision in Foundry Tools to identify product defects on an assembly line. Answer: Yes
* A logistics company can use Azure Vision in Foundry Tools to recognize package shipping labels. Answer: Yes
* The HR department at your company can only use Azure Vision in Foundry Tools to extract written content from Microsoft Word files. Answer: No Azure Vision in Foundry Tools provides computer vision capabilities to analyze images, including identifying visual features and reading text with OCR. Because it is designed to "analyze images" and support vision scenarios, it can be applied to manufacturing quality inspection use cases where the goal is to detect anomalies/defects from images captured on a production line. This aligns with statement 1 being Yes .
Statement 2 is also Yes because recognizing shipping labels is fundamentally text extraction from images (often plus some layout/field parsing). Azure Vision supports optical character recognition (OCR) to read printed text from images, and Microsoft documentation explicitly notes OCR can extract text from images such as product labels and similar real-world text surfaces-making shipping labels a direct fit.
Statement 3 is No because it is incorrectly restrictive. Azure Vision is not limited to extracting written content from Word documents, nor is OCR restricted to Word files. Vision capabilities apply broadly to images (and, depending on the capability, various document/image inputs) for tasks like image analysis and text recognition. HR could use it for many scenarios such as extracting text from scanned images, photos, or other visual inputs-not "only" Word files.
質問 # 71
Hotspot Question
Select the answer that correctly completes the sentence.
正解：
解説：
質問 # 72
An organisation is developing an AI system that will recommend candidates for internal promotions. During testing, the system produces accurate recommendations for most departments but consistently underperforms for the engineering department, where the workforce is more demographically diverse than other departments.
Which responsible AI standards are most directly relevant, and what action should the organisation take?
- A. Transparency and accountability only, by documenting the limitation
- B. Privacy and security only, by ensuring candidate data is encrypted
- C. Transparency only, by informing employees that the system has limitations
- D. Fairness, reliability, and inclusiveness, by auditing the training data for representation gaps, testing the system's performance across all demographic groups, and ensuring the system works equitably for the diverse engineering workforce before deployment
正解：D
解説：
Eight responsible AI standards: fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability. In this scenario, three standards are most directly relevant.
Fairness requires that the AI system treats all people equitably, meaning it must not disadvantage candidates from the more diverse engineering department. Reliability requires that the system performs consistently across all departments, not just most of them. Inclusiveness requires that the system benefits everyone, including diverse populations. The correct action addresses all three: audit training data for representation gaps that could cause bias, test across demographic groups, and ensure equitable performance before deployment.
質問 # 73
Your company uses Microsoft 365 Copilot.
You identify several business processes that require custom workflows and specialized automation.
You need to recommend a solution that extends Copilot capabilities while minimizing development effort and costs.
What should you recommend?
- A. Deploy a Copilot connector.
- B. Build a custom agent by using the full experience of Microsoft Copilot Studio.
- C. Create an agent by using Microsoft Foundry.
- D. Build a declarative agent by using the lite experience of Microsoft Copilot Studio.
正解：D
解説：
To extend Microsoft 365 Copilot while minimizing development effort and costs for business processes requiring specialized automation, you should build a declarative agent by using the
"lite" experience of Microsoft Copilot Studio (often referred to as the Agent Builder).
Declarative Agent (Lite Experience)
Best for: Minimizing effort and cost while staying within the Microsoft 365 ecosystem.
Effort: Low; utilizes a natural language interface where you describe tasks and workflows in plain language.
Capabilities: Adds custom instructions, knowledge (like SharePoint files), and specific actions (API plugins) to the existing Copilot orchestrator.
Cost: Often included with Microsoft 365 Copilot licenses, reducing additional procurement friction.
Incorrect:
[Not A]
Agent with Microsoft Foundry
Best for: Pro-code development, specialized model tuning, and high-scale, multi-agent orchestration.
Effort: High; requires professional developers, data scientists, and specialized skills in languages like Python or C#.
Cost: Higher due to specialized developer resources and Azure consumption-based pricing for models and infrastructure.
[Not C]
Custom Agent (Full Copilot Studio)
Best for: More complex, branched workflows and departmental-scale automation.
Effort: Moderate; involves a low-code, drag-and-drop interface and may require understanding
"Topics" and manual conversation logic.
Capabilities: Offers greater reach, bespoke integrations, and can be published as a standalone bot beyond Microsoft 365 apps.
Reference:
https://peafowlit.com/blog/microsoft-copilot-studio-vs-foundry-ai-agents
質問 # 74
Hotspot Question
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:
Box 1: Yes
Yes - Content filtering controls can prevent AI-generated responses from exposing confidential and sensitive information.
Content filtering controls are a critical safeguard for preventing AI-generated responses from inadvertently exposing confidential or sensitive information. These controls operate as
"guardrails" by inspecting data at both the input (prompt) and output (completion) stages.
Box 2: Yes
Yes - AI-generated content can unintentionally reveal sensitive information if the generative AI model has access to unsecured data source.
Unsecured data sources significantly increase the risk of AI-generated content exposing sensitive information through several mechanisms. Because generative AI models are "data-hungry" and often lack transparent data boundaries, they can absorb and later reproduce confidential data they were exposed to during training or operation.
Box 3: No
No - To prevent data exposure, only the prompts used by users must be protected by using policies.
While protecting user prompts is a critical first step, it is not sufficient on its own to prevent data exposure. Relying solely on prompt policies leaves significant gaps where sensitive data can still leak through other vectors.
Reference:
https://noma.security/resources/ai-application-security/
https://pacific.ai/managing-privacy-risks-llm-guidance/
https://www.paloaltonetworks.com/blog/network-security/securing-the-future-by-protecting- sensitive-data-in-ai-systems
質問 # 75
You are exploring how Microsoft 365 Copilot uses Microsoft Graph to deliver AI-powered experiences.
Which information in Microsoft Graph can Copilot use by default?
- A. social media activity
- B. data stored in a file share
- C. emails, files, meetings, and chats in Microsoft 365
- D. content from public websites
正解：C
解説：
Microsoft 365 Copilot is designed to act as an AI-powered assistant that leverages Microsoft Graph to access your organization's data, including emails, files, chats, and meetings. By default, Copilot integrates with Microsoft 365 apps and uses this data to provide contextually relevant, real-time assistance.
Key Capabilities via Microsoft Graph
*-> Data Access: Copilot retrieves information from emails, files (OneDrive/SharePoint), meetings (Teams transcripts), and chats.
Grounding: It uses this data to "ground" prompts, providing responses that are specific to your actual work rather than general information.
Semantic Indexing: Copilot creates a semantic index of your Graph data to understand relationships and intent, making search and retrieval more accurate.
Reference:
https://learn.microsoft.com/en-us/copilot/microsoft-365/enterprise-data-protection
質問 # 76
Hotspot Question
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:
Box 1: No
No - A generative AI solution is well-suited to predict next-quarter sales trends.
Predictive AI, not generative AI, is designed to forecast future trends and outcomes.
While Generative AI (GenAI) and Predictive AI both use historical data and machine learning, their primary purposes are distinct: Generative AI is designed to create new, original content (text, images, code), whereas Predictive AI is designed to forecast future trends and outcomes.
Box 2: Yes
Yes - A generative AI solution is well-suited to summarize lengthy policy documents.
Generative AI solutions are exceptionally well-suited to summarize lengthy policy documents, offering a transformative approach to document management that enhances efficiency, reduces manual labor, and improves accuracy. These systems can analyze hundreds of pages in minutes, extracting key obligations, deadlines, and critical information while removing redundancy.
Box 3: Yes
Yes - A generative AI solution can create product descriptions from product specifications.
Generative AI solutions significantly streamline e-commerce by transforming raw product specifications-such as materials, dimensions, and technical features-into engaging, human- readable product descriptions. By leveraging Large Language Models (LLMs) and Natural Language Processing (NLP), these tools can automate content creation for thousands of items in minutes, ensuring consistency in brand voice and improving search engine optimization (SEO) Reference:
https://nfina.com/generative-ai-vs-predictive-ai/
https://artificio.ai/blog/generative-ai-for-document-summarization-and-insights
https://describely.ai/blog/ai-generated-product-descriptions-for-ecommerce
質問 # 77
In which scenario is Azure Machine Learning most likely to deliver strategic value for an organization?
- A. Entering customer feedback into a spreadsheet to understand sentiment.
- B. Digitizing a paper-based process to reduce errors.
- C. Using historical sales data to forecast demand across product categories.
- D. Sending personalized emails to customers based on the customer location.
正解：C
解説：
Azure Machine Learning (Azure ML) delivers strategic value by transforming historical sales data into a competitive advantage through advanced demand forecasting. By identifying complex patterns in past consumer behavior, it helps businesses optimize high-stakes operational areas such as inventory management, production planning, and resource allocation.
Benefits
Inventory Optimization: Businesses can maintain leaner inventory levels, drastically reducing storage costs and minimizing the risk of both stockouts and overstocking.
Financial Performance: Improved forecast accuracy directly protects margins by reducing the need for emergency shipping, overtime labor, and waste from unsold goods.
Strategic Growth: Accurate long-term forecasts provide a reliable roadmap for planning product launches, marketing promotions, and market expansion.
Operational Agility: Azure ML's Automated Machine Learning (AutoML) allows companies to quickly adapt to market shifts-like seasonal trends or unexpected disruptions-by continuously learning from new data.
Reference:
https://learn.microsoft.com/en-us/azure/architecture/ai-ml/idea/next-order-forecasting
質問 # 78
Your company creates a custom Azure Machine Learning model that uses a generative AI assistant. The model initially delivers strong results. However, six months later, the model predictions become noticeably less accurate. What is a possible cause of the issue?
- A. The model requires additional compute resources.
- B. The model was trained incorrectly.
- C. The input data changed over time.
正解：C
解説：
A common reason models degrade after being successful in production is data drift (also called concept drift). Over time, the distribution of input data changes -for example, customer behavior shifts, product catalog changes, seasonality changes, new categories appear, sensors get recalibrated, or business processes evolve. When the model sees data that differs from what it was trained on, its predictions can become less accurate. This is exactly what option A describes and is the most likely "six months later" cause.
Option B is not a primary explanation for reduced predictive accuracy. More compute can improve throughput
/latency, but it does not inherently improve correctness of predictions. If anything, compute constraints typically cause timeouts or slower responses, not a systematic accuracy drop.
Option C (trained incorrectly) would usually manifest earlier-poor performance from the start-unless the
"incorrectness" is that the model was trained on a snapshot that later became stale (which again maps back to drift). The correct operational response is to monitor for drift, validate performance regularly, and retrain
/refresh the model using newer representative data and updated features/labels.
質問 # 79
Select the answer that correctly completes the sentence.
The Researcher agent in Microsoft 365 Copilot __________.
正解：
解説：
Explanation:
uses reasoning capabilities to generate deep insights based on organizational data and the web.
The sentence is best completed by the option describing Researcher as a research-oriented reasoning agent that combines information from the web and your work data to produce deeper insights. Microsoft describes Researcher as an agent built into Microsoft 365 Copilot to tackle complex, multistep research and to help users gather, analyze, and summarize information from "the web, your work documents, or both," producing a structured output that supports decision-making. That is exactly what the completion "uses reasoning capabilities to generate deep insights based on organizational data and the web" captures.
The other dropdown options are better matches for different tools/agents: "creates visual dashboards from structured data in Excel and Power BI" is more aligned to BI/reporting workflows; "generates a pivot table and performs time series forecasting" is spreadsheet/analytics functionality; and "performs complex, multi- step, data analysis and code execution tasks over arbitrary datasets" is the hallmark positioning of the Analyst agent ("virtual data scientist") rather than Researcher. Researcher's differentiator is deep research across both organizational context and the open web, while Analyst's differentiator is data analysis and computation .
質問 # 80
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?
- A. Standard On-Demand
- B. Provisioned PTUs
- C. Microsoft 365 Copilot
- D. Batch API
正解：A
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 # 81
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AB-731試験問題無料PDFダウンロード 最近更新された問題です：https://www.jpntest.com/shiken/AB-731-mondaishu
AB-731認定試験問題集には117練習テスト問題：https://drive.google.com/open?id=1knthgcF_khwhDPqu_PMBMjz4UjwtntRl