2026年ShikenPASSの最新AB-731 PDFダンプおよびAB-731試験エンジンの無料共有：https://drive.google.com/open?id=1gwfkGqDtRSpgJV51qN_4hgDn5THaclV8
あなたがより少ない時間と労力を置いてMicrosoftのAB-731試験を準備するために我々ShikenPASSは多くの時間と労力を投資してあなたにソフトウェアを作成します。我々の全額で返金する承諾は話して行動しないわけではない、我々はいくつ自社製品に自信を持っても、あなたに満足させる効果がないなら、我々は速やかに全額で返金します。しかし、我々はMicrosoftのAB-731試験のソフトウェアは、あなたの期待に応えると信じて、私はあなたの成功を祈っています！
| トピック | 出題範囲 |
|---|---|
| トピック 1 |
|
| トピック 2 |
|
| トピック 3 |
|
古く時から一寸の光陰軽るんずべからずの諺があって、あなたはどのぐらい時間を無駄にすることができますか？現時点からShikenPASSのAB-731問題集を学んで、時間を効率的に使用するだけ、AB-731知識ポイントを勉強してMicrosoftのAB-731試験に合格できます。短い時間でAB-731資格認定を取得するような高いハイリターンは嬉しいことではないでしょうか。
質問 # 52
A retail company wants to use AI to forecast seasonal demand for its product lines using five years of historical sales data. A separate team wants to use AI to generate personalised marketing emails based on customer purchase history.
Which types of AI are most appropriate for each use case?
正解：A
解説：
Each use case requires a fundamentally different type of AI. Demand forecasting from historical sales data is a classic machine learning use case involving pattern recognition and time-series prediction on structured numerical data. Machine learning models excel at identifying trends, seasonality, and correlations in historical datasets to produce accurate forecasts. Personalised email generation requires creating new, original content tailored to individual customers, which is the defining capability of generative AI.
質問 # 53
- Select the answer that correctly completes the sentence.
Using high-quality grounding data in a generative AI solution __________.
正解：
解説：
Explanation:
improves the accuracy and reliability of the predictions and outputs of AI.
High-quality grounding data improves a generative AI solution by anchoring responses to trusted, relevant, and up-to-date information , which increases the likelihood that outputs are accurate, consistent, and aligned with the organization's expectations. This is why the best completion is " improves the accuracy and reliability of the predictions and outputs of AI ." When the model is given authoritative context (for example, approved policy text, product specifications, knowledge base articles, or controlled enterprise content), it has less need to "guess" based on general patterns in its training data. That reduces hallucinations and improves response relevance to the user's question and the business domain.
It does not "ensure that all responses are factually accurate" because grounding reduces errors but cannot eliminate them completely-retrieval can return incomplete or irrelevant passages, user prompts can be ambiguous, and the model can still misinterpret context. It also does not inherently "increase performance of an AI model" in the sense of speed/throughput or model capability; grounding is an architecture and data strategy that improves output quality, not compute efficiency. Finally, grounding is not about "increasing storage required to host an AI model." While you may store documents in an index or repository, the core benefit is improved response quality through better context, not larger model hosting requirements.
質問 # 54
HOTSPOT - Select the answer that correctly completes the sentence.
Microsoft 365 Copilot can be used to __________.
正解：
解説：
Explanation:
create a Microsoft Word document.
Microsoft 365 Copilot is a productivity-focused AI capability embedded across Microsoft 365 apps such as Word, Excel, PowerPoint, Outlook, and Teams. A core, common use case is drafting and generating documents in Word from prompts and existing context. Therefore, "create a Microsoft Word document" is the best completion.
The other options are not appropriate for Microsoft 365 Copilot's scope. "Monitor network traffic and alerts in real time" is a security/operations function more aligned to security monitoring tools and, in Microsoft's product set, closer to Microsoft Security Copilot or dedicated SIEM/SOAR platforms (for example, Microsoft Sentinel). "Modify administrative permissions for SharePoint files" is an admin/governance action that is intentionally protected by role-based access control and is not something end-user Copilot features are meant to perform as a default capability. "Create a list in Microsoft SharePoint" is closer, because Copilot can assist in generating content and can work within Microsoft 365 collaboration contexts; however, the most universally accurate and directly supported capability among the options is creating/drafting a Word document, which is a primary Copilot scenario in Word.
In short, Microsoft 365 Copilot is designed to help users create, summarize, rewrite, and transform content within Microsoft 365 apps, and document creation in Word is a canonical example of that value.
質問 # 55
Select the answer that correctly completes the sentence.
Prompt engineering is the process of __________.
正解：
解説：
Explanation:
crafting clear instructions to guide generative AI solutions in generating context-appropriate content.
Prompt engineering is fundamentally about how you communicate intent to a generative AI model so it produces outputs that meet business expectations. The best completion is "crafting clear instructions to guide generative AI solutions in generating context-appropriate content" because it captures the practical, day-to- day discipline: shaping the input (prompt) with the right task framing, constraints, context, and output format.
In real deployments, prompt engineering includes specifying the role and objective (for example, "act as a customer support agent"), providing the necessary context (product details, policy excerpts, audience), adding explicit requirements (tone, length, must/must-not statements), and defining structured output (JSON fields, bullet sections, headings). It can also include adding examples (few-shot prompting), clarifying what to do when information is missing, and instructing the model to cite only provided sources or to ask follow-up questions. These techniques reduce ambiguity, improve consistency, and lower the risk of hallucinations or off-brand responses.
The other options are not accurate definitions. "Integrating AI-powered tools into business workflows" describes solution adoption/integration, not prompt engineering. "Identifying and fixing errors in AI- generated content" is review/editing or quality assurance. "Designing, developing, and training generative AI models" is model development/ML engineering. Prompt engineering operates without changing model weights ; it's about steering model behavior through well-constructed instructions and context.
質問 # 56
Your company deploys an AI-powered loan approval solution that enables applicants to request an explanation as to why their loan application was denied.
Which Microsoft responsible AI principle is this an example of?
正解：C
解説：
The correct answer is A. transparency. Transparency means users and stakeholders should understand when AI is being used, what the AI system can do, what its limitations are, and how important outputs or decisions were produced. In this scenario, applicants can request an explanation for why a loan application was denied.
That explanation capability makes the AI-assisted decision more understandable and reviewable, which directly supports transparency. Fairness is related to avoiding unjust bias or discriminatory outcomes. Privacy and security focus on protecting personal and organizational data. Inclusiveness focuses on ensuring AI systems work for people with different abilities, backgrounds, and experiences. Because the scenario is specifically about explaining a decision, the best answer is transparency.
質問 # 57
......
AB-731認定試験は試験に関連する書物を学ぶだけで合格できるものではないです。がむしゃらに試験に要求された関連知識を積み込むより、価値がある問題を勉強したほうがいいです。効率のあがる試験問題集は受験生の皆さんにとって欠くことができないツールです。ですから、はやくShikenPASSのAB-731問題集を入手しましょう。これは高い的中率を持っている問題集で、ほかのどのような勉強法よりもずっと効果があるのです。これはあなたが一回で楽に成功できるを保証するめぼしい参考書です。
AB-731日本語版問題集: https://www.shikenpass.com/AB-731-shiken.html
無料でクラウドストレージから最新のShikenPASS AB-731 PDFダンプをダウンロードする：https://drive.google.com/open?id=1gwfkGqDtRSpgJV51qN_4hgDn5THaclV8