BONUS！！！ PassTest AB-731ダンプの一部を無料でダウンロード：https://drive.google.com/open?id=11TBYhTwIuY4n3o_16gS0Ymx55_w1-EFt
AB-731の実際のテストは、さまざまな分野の多くの専門家によって設計され、顧客のさまざまな状況を考慮し、顧客が時間を節約できるように実用的なAB-731学習教材を設計しました。 学生であろうとオフィスワーカーであろうと、AB-731試験の準備にすべての時間を費やすことはないと思います。専門知識の勉強、家事、子供の世話などに取り組んでいます。 簡素化された情報により、効率的に学習することができます。 そして、あなたは事前に本当の試験を感じたいですか？ AB-731試験問題を購入するだけです！
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | AI Transformation Leader |
| Exam Number: | AB-731 |
| Passing Score: | 700 |
| Exam Format: | Interactive components (may be included), Multiple-choice / scenario-based questions, Proctored online exam |
| Exam Price: | $99 USD |
| Exam Duration: | 45 minutes |
| Available Languages: | English |
| Related Certifications: | Microsoft AI Business Professional (AB-730) |
| Recommended Training: | AB-731T00: Drive AI transformation in your organization |
| Exam Registration: | Official Microsoft certification page Exam scheduling (Pearson VUE via Microsoft portal) |
| Sample Questions: | Microsoft AB-731 Sample Questions |
| Exam Way: | Online proctored exam (may include interactive components) |
| Pre Condition: | No formal prerequisites required; designed for business leaders and decision-makers. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/ai-transformation-leader/ |
PassTest はMicrosoft業界に認定試験大綱の主要なサプライヤーとして、AB-731専門家は一緻して品質の高い商品を開発し続けています。
| トピック | 出題範囲 |
|---|---|
| トピック 1 |
|
| トピック 2 |
|
| トピック 3 |
|
質問 # 54
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?
正解：D
解説：
This scenario is a clear violation of the fairness principle. Fairness in Microsoft's Responsible AI framework is about ensuring AI systems do not create unjustified bias or discriminatory outcomes-especially when decisions affect people's access to opportunities such as credit, employment, housing, or education. A rule or learned behavior that rejects all applicants over a certain age creates a systematic, categorical disadvantage for a protected demographic group and indicates a discriminatory decision boundary rather than an individualized assessment of creditworthiness.
Even if the model designers believed age correlates with risk, using a hard cutoff that rejects every applicant older than 60 is not an equitable approach. It suggests the model is either using age directly as a dominant feature or reflects biased training data/labels that encoded discriminatory outcomes. Fairness requires you to evaluate model outcomes across groups (for example, age brackets), measure disparate impact, and apply mitigations such as feature review (removing or constraining sensitive attributes), rebalancing training data, adjusting thresholds, or using fairness-aware training/evaluation methods. It also requires governance and review of high-stakes automated decisions.
The other principles are not the best match: transparency concerns explainability and user understanding, accountability concerns human oversight and ownership, and reliability and safety concerns consistent and safe operation. The core issue here is discriminatory treatment across an age group- fairness .
質問 # 55
- 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 __________.
正解：
解説：
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 .
質問 # 56
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?
正解：C
解説：
Microsoft 365 Copilot is designed to work within the Microsoft 365 ecosystem and use organizational context that is already governed by Microsoft Entra ID, Microsoft 365 permissions, and compliance controls. By default, Copilot can use Microsoft Graph signals and content that exist in Microsoft 365 workloads the user already has access to-most commonly emails, files, meetings, and chats . That corresponds to A .
The key concept is permission-trimming: Copilot doesn't magically gain access to everything; it can only surface or use data that the signed-in user is permitted to access in Microsoft 365. This is what makes Copilot valuable for productivity scenarios-summarizing email threads, drafting replies, generating meeting recaps, creating documents from your files, or pulling context from Teams chats-because those artifacts are already part of daily work and already subject to tenant policies.
The other options are not "by default" Microsoft Graph content for Copilot: B (file shares) typically requires additional integration or migration into Microsoft 365 repositories or indexing via connectors; it's not inherently available. C is unrelated to Microsoft 365 tenant work data. D (public web) is not "information in Microsoft Graph"; web grounding is a separate capability and not the default Graph workload data source referenced here.
質問 # 57
- For each of the following statements, select Yes if the statement is true. Otherwise, select No.
正解：
解説：
Explanation:
Answer Area
* A generative AI model guarantees factually accurate responses if the model is trained on a large dataset.
answer: No
* Content filtering and responsible AI safeguards help a generative AI model generate safe and inoffensive content. Answer: Yes
* A generative AI model always produces fair and unbiased results when the training data has been properly prepared and reviewed for fairness. Answer: No
* No - A larger training dataset can improve coverage and fluency, but it does not guarantee factual accuracy. Generative models can still hallucinate, mix concepts, or produce plausible-but-incorrect statements because they generate likely text rather than verifying truth. This is why solution designs commonly add grounding/retrieval, validation, and human review for high-stakes outputs.
* Yes - Content filtering and Responsible AI controls are specifically used to reduce harmful, unsafe, or policy-violating outputs . In practice, safeguards include input/output filters, safety classifiers, and governance controls that help enforce safety policies and minimize offensive content. These controls don't make outputs "perfect," but they materially reduce risk and are a standard part of production AI deployments.
* No - Even with careful data preparation and fairness reviews, models can still produce biased outcomes due to residual bias in data, label/measurement issues, deployment context, and shifting real- world distributions. "Always fair and unbiased" is an absolute claim that is not achievable in real systems; fairness is managed through continuous evaluation, monitoring, and mitigations-not assumed as guaranteed.
質問 # 58
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
正解：
解説：
Explanation:
The correct selections are Yes, No, Yes. Prompt engineering is the practice of designing clear and effective inputs that guide a generative AI model toward relevant, accurate, and useful output. Guidelines, constraints, role instructions, tone requirements, and output-format rules are all common prompt-engineering techniques, so statement A is true. Statement B is false because examples are normally considered examples or few-shot demonstrations, not the instruction itself. The instruction tells the model what task to perform, while examples demonstrate the preferred pattern or output style. Statement C is true because context supplies the background information, source material, business scenario, audience, or constraints the model should use when generating the answer. Strong prompts usually combine instruction, context, constraints, and examples.
質問 # 59
......
AB-731問題例: https://www.passtest.jp/Microsoft/AB-731-shiken.html
P.S. PassTestがGoogle Driveで共有している無料かつ新しいAB-731ダンプ：https://drive.google.com/open?id=11TBYhTwIuY4n3o_16gS0Ymx55_w1-EFt