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AB-731学習ガイドの資料は、常に卓越性と同義語です。 AB-731実践ガイドは、さまざまな資格試験に合格するかどうかに関係なく、ユーザーが簡単に目標を達成するのに役立ちます。当社の製品は、必要な学習教材を提供します。もちろん、AB-731の実際の質問は、ユーザーに試験に関する貴重な経験だけでなく、試験に関する最新情報も提供します。 AB-731の実用的な教材は、他の教材よりも高い歩留まりをもたらす学習ツールです。決心したら、私たちを選んでください！
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進歩を勇敢に追及する人生こそ素晴らしい人生です。未来のある日、椅子で休むとき、自分の人生を思い出したときに笑顔が出たら成功な人生になります。あなたは成功な人生がほしいですか。そうしたいのなら、速くPass4TestのMicrosoftのAB-731試験トレーニング資料を利用してください。これはIT認証試験を受ける皆さんのために特別に研究されたもので、１００パーセントの合格率を保証できますから、躊躇わずに購入しましょう。
質問 # 88
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?
正解：B
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 # 89
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 # 90
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:
* A text-to-image generator can be used to translate content into other languages. Answer: No
* A predictive analytics model can generate new marketing content for a company's online ads. Answer:
No
* A generative AI chatbot can engage customers in personalized conversations and recommend products.Answer: Yes
* No - A text-to-image generator's primary function is to create images from text prompts , not translate text between languages. Translation is a natural language processing task typically handled by language models or dedicated translation services. A text-to-image model could illustrate translated content (e.g., generate an image based on a translated prompt), but it is not the tool used to perform the translation itself.
* No - Predictive analytics models are designed to predict outcomes (forecasts, probabilities, classifications) from historical patterns, such as predicting click-through rate, churn, or next-quarter demand. They are not designed to create new ad copy or marketing creatives. Generating new marketing content is a generative AI capability (text generation), not predictive analytics.
* Yes - A generative AI chatbot is well-suited to interactive, natural-language conversations . With access to product catalogs and business rules, it can ask clarifying questions, tailor responses to customer needs, and recommend products (for example, suggesting tents based on group size, season, and budget). This combines conversational generation with retrieval/recommendation logic behind the scenes, enabling personalized customer engagement at scale.
質問 # 91
Your company discovers that several employees use personal ChatGPT accounts to assist with work tasks. You are concerned about proprietary data being shared externally.
You need to evaluate the business value of rolling out Microsoft 365 Copilot.
Which capability is a key benefit of using Copilot instead of a personal ChatGPT account?
正解：B
解説：
A major, defining advantage of Microsoft 365 Copilot over a personal ChatGPT account is its deep, native integration with an organization's internal data-including emails, documents, chats, and meetings-while strictly adhering to existing Microsoft 365 security, compliance, and privacy policies.
Here is a breakdown of why this is a critical differentiator:
1. Access to Internal Data ("Grounding")
Microsoft 365 Copilot: Accesses your organization's data via Microsoft Graph. It can summarize, analyze, and create content based on your Word documents, emails in Outlook, spreadsheets in Excel, and meetings in Teams.
Personal ChatGPT: Does not have access to your private company files, emails, or internal systems unless you manually copy and paste that information into the chat.
2. Adherence to Security and Compliance Policies
Microsoft 365 Copilot: Inherits your organization's existing security configurations, such as sensitivity labels, Data Loss Prevention (DLP) policies, and identity-based access controls. If you do not have permission to view a file, Copilot will not use that file to answer your prompt.
Personal ChatGPT: Operates outside your corporate security boundary. Using a personal account to analyze company data can risk leaking confidential information to a third-party, which is typically against corporate security policies.
Reference:
https://www.microsoft.com/en-us/microsoft-365-copilot/copilot-vs-chatgpt-enterprise
質問 # 92
Your company wants to ensure that AI solutions are used responsibly and align with company values and compliance requirements.
You need to establish governance principles for AI use.
Which two actions should you perform? Select the two BEST answers. Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
正解：B、E
解説：
To establish robust governance for AI, a company must define accountability norms that bridge the gap between business objectives and technical execution. Effective frameworks ensure that specific individuals-not just committees-are responsible for AI outcomes across the entire lifecycle.
Core Accountability Strategies
Establish Clear Ownership: Assign a specific person or team as the "accountable owner" for each AI application. This includes defining decision rights for who can approve, modify, or retire a system.
To establish an effective AI governance framework, you should implement a structured review process that translates high-level ethical principles into operational practice. This process serves as a "brake pedal" for the organization, ensuring innovation remains safe and aligned with corporate values.
Reference:
https://www.databricks.com/blog/ai-governance-best-practices-how-build-responsible-and- effective-ai-programs
質問 # 93
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