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質問 # 66
- 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 can amplify existing data governance challenges.
answer: Yes
Implementing Microsoft 365 Copilot reduces data management costs.
answer: No
Microsoft 365 Copilot can help IT teams manage data risks.
answer: Yes
Yes - Copilot relies on the permissions, sharing links, and content exposure that already exist in Microsoft
365. If an organization has oversharing (for example, broadly accessible SharePoint sites, poorly scoped Teams, unmanaged external sharing, or excessive access rights), Copilot can surface that content more easily through natural-language querying. In other words, Copilot doesn't create new permissions, but it can increase visibility of governance gaps and make the impact of weak information architecture more apparent.
No - It is not accurate to claim that implementing Copilot inherently reduces data management costs.
Adoption often requires up-front investment in data hygiene, sensitivity labeling, retention, permission cleanup, DLP, and change management. Some organizations may realize productivity gains or reduced effort over time, but "reduces costs" is not a guaranteed outcome and depends heavily on the current state of governance, the scale of remediation needed, and how Copilot is rolled out.
Yes - Copilot can support IT risk management when deployed with the right controls: identity and access governance, sensitivity labels, DLP policies, retention, auditing, and compliance tooling. Because Copilot operates within the Microsoft 365 security/compliance boundary and honors existing access controls, IT can apply centralized policies to reduce leakage risk and improve overall control of how organizational data is accessed and used.
質問 # 67
A marketing team wants to automatically create product descriptions and campaign email drafts.
Which generative AI capability best meets this business need?
正解：D
解説：
Natural language content generation is correct because Natural language content generation enables the creation of product descriptions, emails, blogs, and other written materials using prompts. This directly supports marketing automation and content scaling.
References:
https://learn.microsoft.com/en-us/training/modules/understand-foundations-generative-ai- business-leaders/3-explore-business-value-generative-ai-solutions
https://www.microsoft.com/en-us/power-platform/blog/power-automate/generative-ai-prompts-to- automate-content-processing/
質問 # 68
Your company is building a portfolio of AI-powered business solutions. Company executives want to understand how Microsoft responsible AI principles can support the company ' s long-term goals. Which benefit best demonstrates the importance of responsible AI? Select the BEST answer.
正解：A
解説：
Responsible AI is fundamentally about earning and maintaining trust while scaling AI across the enterprise.
Option C is the best answer because responsible AI practices (fairness, reliability and safety, privacy and security, transparency, accountability, and inclusiveness) reduce reputational, legal, and operational risk and make adoption sustainable over time. When stakeholders trust that AI is governed, tested, and monitored, the organization can expand AI usage confidently across business units.
The other options are incorrect because they make absolute or counterproductive claims. A is false:
responsible AI does not "guarantee" accuracy; it reduces risk and improves assurance, but no model can be guaranteed correct in all contexts. B is the opposite of reality: responsible AI increases the importance of data protection and governance; it does not reduce the need for them. D is also incorrect: responsible AI requires clear ownership and oversight, especially from leadership, because accountability is a core principle. In short, responsible AI matters because it builds stakeholder confidence and provides guardrails that support long- term, scalable, and compliant AI adoption-exactly what executives care about when investing in an AI portfolio.
質問 # 69
Hotspot Question
Select the answer that correctly completes the sentence.
正解：
解説：
Explanation:
Box: guide AI strategy, ensure responsible AI oversight, and promote alignment across business units.
To ensure that your organization follows trustworthy AI principles, the organization should establish an AI governance council to _____________________.
An AI governance council should be a cross-functional, multidisciplinary body that oversees the development, deployment, and evaluation of AI solutions to ensure they are ethical, secure, and aligned with organizational values. Key functions include establishing accountability, managing risks, ensuring regulatory compliance (e.g., privacy), and promoting transparency and fairness.
Core Responsibilities
The council's mandate typically covers the entire AI lifecycle:
*-> Strategy & Policy: Developing internal AI usage policies and identifying high-risk use cases.
*-> Risk Oversight: Conducting impact assessments for bias, privacy, and security before deployment.
Continuous Monitoring: Tracking live models for "drift" (performance degradation over time) or emerging ethical issues.
Culture & Literacy: Driving organization-wide training to build AI fluency and a responsible AI culture.
Audit & Reporting: Maintaining transparent records and "model cards" to demonstrate accountability to stakeholders and regulators.
Reference:
https://athena-solutions.com/ai-governance-framework-2025/
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
* Retrieval Augmented Generation (RAG) architectures require that a large language model (LLM) be retrained on proprietary data. Answer: No
* Retrieval-Augmented Generation (RAG) grounds a language model to produce more factual and context-relevant responses. Answer: Yes
* Retrieval Augmented Generation (RAG) retrieves information from external knowledge sources at runtime instead of relying solely on the knowledge of a generative AI model. Answer: Yes
1) No - RAG does not require retraining or fine-tuning the base LLM on proprietary data. The defining idea of RAG is to keep the model as-is and instead supply it with relevant context retrieved from trusted sources at inference time. Fine-tuning can be optional for style or specialized behavior, but it is not a requirement for RAG.
2) Yes - RAG is a grounding approach. By retrieving authoritative passages (policies, manuals, product specs, internal knowledge bases) and injecting them into the prompt context, the model's answer is constrained by evidence that is relevant to the user's question. This improves factuality and domain relevance and helps reduce hallucinations.
3) Yes - RAG explicitly depends on runtime retrieval from external knowledge sources, such as indexed documents, databases, or enterprise repositories. The retrieval layer finds the best matching content for the query, and the generation layer uses that retrieved content to craft the response. This is why RAG is valuable when information changes frequently: you update the source documents/index rather than retraining the LLM.
Overall, RAG is best understood as an architecture pattern that combines search/retrieval + generation , improving accuracy and freshness without the cost and risk of retraining the underlying model each time the knowledge base changes.
質問 # 71
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