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NEW QUESTION # 65
What is considered a best practice when forming an AI adoption team in an enterprise environment?
Answer: C
Explanation:
Enterprise AI adoption succeeds when it is cross-functional from the start . Option A is best practice because AI impacts legal risk, privacy, security, compliance, workforce processes, and business strategy-not just technology. Including leadership ensures alignment to priorities and funding; including business units ensures use cases and success metrics are real; including legal/compliance ensures responsible AI and regulatory obligations are addressed early. This prevents rework and reduces the chance of deploying solutions that are misaligned with policy or unacceptable risk.
Options B and C delay governance and business alignment, which often leads to "build first, govern later" failure modes-solutions that work technically but cannot be approved or scaled due to privacy/security gaps or unclear accountability. Option D over-optimizes for vendor selection without ensuring the organization has defined responsible AI requirements, target use cases, and operating model. Procurement is important, but it is not the primary driver of a successful adoption team. The most sustainable approach is a representative adoption team that integrates business, technical, and governance stakeholders from day one.
NEW QUESTION # 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.
Answer:
Explanation:
Explanation:
Microsoft 365 Copilot can amplify existing data governance challenges.
The answer: Yes
Implementing Microsoft 365 Copilot reduces data management costs.
The answer: No
Microsoft 365 Copilot can help IT teams manage data risks.
The 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.
NEW QUESTION # 67
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?
Answer: B
Explanation:
The Standard (On-Demand) tier is the best choice for this scenario because it follows a pay-as- you-go consumption model. This allows a company to start a Proof of Concept (PoC) with virtually zero upfront cost or commitment, paying only for the tokens processed. As demand grows, the service provides the flexibility to scale without needing to manage complex capacity planning early on.
Reference:
https://azure.microsoft.com/en-us/products/ai-foundry/models/openai
NEW QUESTION # 68
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 69
An organization is adopting generative AI solutions and wants to ensure systems are designed to minimize bias, protect user data, and operate transparently.
Which Microsoft Responsible AI principle best aligns with this strategy?
Answer: B
Explanation:
Fairness is correct because the fairness principle in Microsoft Responsible AI focuses on ensuring AI systems treat individuals and groups equitably, minimizing harmful bias and discriminatory outcomes. This includes evaluating training data, model behavior, and generated outputs to prevent unequal treatment. While the scenario also references transparency and data protection, minimizing bias is most directly aligned with the fairness principle.
References:
https://learn.microsoft.com/en-us/training/modules/embrace-responsible-ai-principles-practices/1- introduction
https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-use-of-ai-overview?view=foundry- classic
NEW QUESTION # 70
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