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NEW QUESTION # 93
A company has a Microsoft Foundry agent that summarizes customer feedback and recommends products to customers. The agent references data from multiple knowledge sources.
Users report that the agent response time is slow.
Telemetry data shows that the agent frequently reaches its token usage limit You need to recommend a solution to reduce token usage without degrading the quality of the generated responses.
What should you recommend?
Answer: B
Explanation:
The problem is not just that the agent is slow. The telemetry specifically says it frequently reaches its token usage limit. That means too much content is being pulled into the prompt or context window before the model generates the answer.
The best recommendation is D. Reconfigure the prompts to limit the amount of retrieved content from the knowledge sources.
Why D is correct:
It directly targets the root cause: too many tokens from retrieved context It reduces unnecessary context while still keeping the most relevant information It helps preserve response quality better than simply cutting capabilities or hard-limiting output size Why the other options are less suitable:
A). Chunk documents during indexing can improve retrieval quality in some RAG scenarios, but it does not directly guarantee lower total retrieved token volume in the final prompt B). Lower the maximum token usage limit for the responses may reduce output length, but it does not solve excessive input-context usage and can hurt response quality C). Reduce the number of knowledge sources used by the agent is too blunt and may remove useful grounding unnecessarily
NEW QUESTION # 94
You need to recommend a security solution for agents in a Microsoft Power Platform environment.
The agents must use only approved connectors and services. The solution must prevent the agents from accessing sensitive data.
What should you recommend?
Answer: D
Explanation:
In a Power Platform environment, DLP policies act as the primary guardrail for controlling how data is shared across services and connectors. Here is how they address your specific requirements:
Approved Connectors: DLP policies allow administrators to categorize connectors into three groups: Business, Non-Business, and Blocked. By moving only "approved" connectors into the Business group and blocking others, you ensure agents can only use authorized tools.
Preventing Access to Sensitive Data: You can use DLP policies to prevent the mixing of data between groups. For example, if a "Business" connector (like SharePoint) and a "Non-Business" connector (like Twitter/X) are in different groups, the policy will prevent an agent from creating a flow that moves data from the sensitive business source to the public service.
Granular Control: Modern DLP policies also support Connector Action Control, allowing you to block specific actions (like "Delete") within an approved connector while allowing others.
NEW QUESTION # 95
A company has an AI solution that uses Azure OpenAI models.
You need to recommend a governance solution that monitors and audits changes to model configurations and data usage. The solution must minimize administrative effort.
What should you include in the recommendation?
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answer is E. Microsoft Purview .
This question is centered on governance , specifically the need to:
* monitor changes to model configurations
* audit data usage
* minimize administrative effort
That combination points most strongly to Microsoft Purview .
Why E is correct
Microsoft Purview is Microsoft's core platform for data governance, compliance, auditing, information protection, and lifecycle oversight . When an organization is using Azure OpenAI models and needs a governance-oriented solution for monitoring and auditing how data is used, Purview is the best fit among the listed options.
From an AI business solutions perspective, governance is broader than infrastructure monitoring. It includes:
* understanding how sensitive data is handled
* tracking access and usage patterns
* supporting audit and compliance needs
* helping investigate data exposure concerns
* enforcing information governance practices across AI-enabled workloads Purview is especially strong when the requirement includes auditing data usage because that is a governance and compliance concern, not just a performance or telemetry concern.
It also minimizes administrative effort because it provides centralized governance capabilities rather than requiring the company to stitch together multiple lower-level services for oversight.
Why the other options are incorrect
A). Azure Monitor
Azure Monitor is useful for telemetry, logs, metrics, and operational monitoring. It helps observe system performance and activity, but it is not the best primary governance solution for auditing data usage and broader compliance oversight.
B). Azure Stream Analytics
This service is used for real-time stream processing and analytics. It does not address governance and audit requirements for Azure OpenAI model configurations and data usage.
C). Azure API Management
API Management helps publish, secure, and manage APIs. It is valuable for access mediation and control, but it is not the main governance and auditing platform for data usage and model-configuration oversight.
D). Azure Policy
Azure Policy is very strong for enforcing resource configuration standards and compliance rules at deployment and configuration time. However, the question also emphasizes auditing data usage , which is better aligned to Purview's governance capabilities. Policy is more about enforcement of resource state; Purview is stronger for governance, auditing, and data oversight.
Expert reasoning
Use this exam shortcut:
* Need operational logs and metrics # Azure Monitor
* Need deployment/configuration enforcement # Azure Policy
* Need data governance, auditing, compliance, and information oversight # Microsoft Purview Because the question emphasizes both changes and data usage auditing with a governance lens, Microsoft Purview is the strongest answer.
NEW QUESTION # 96
Scenario: A customer support organization aims to significantly improve its case resolution times and overall agent efficiency. They are looking for an AI solution that can provide real-time assistance to agents by summarizing ongoing conversations, suggesting relevant next best actions, and quickly retrieving historical customer data directly within their workflow.
Which specific Microsoft capability is designed to provide these AI-driven features for enhancing agent productivity within a customer service environment?
Answer: C
Explanation:
Microsoft Copilot for Dynamics 365 Customer Service is correct because this specific Copilot integration provides exactly the described functionalities: real-time conversation summaries, AI- suggested responses, recommended next actions, and the ability to surface relevant knowledge base articles and historical customer data directly within the agent's workflow in Dynamics 365 Customer Service.
References:
https://adoption.microsoft.com/en-us/copilot-in-dynamics-365-customer-service/
https://learn.microsoft.com/en-us/microsoft-cloud/dev/copilot/copilot-for-dynamics365
NEW QUESTION # 97
Hotspot Question
You use Microsoft Copilot Studio analytics to analyze the performance of a deployed Copilot Studio agent. You need to identify which performance metrics to use to measure the following:
- The percentage of engaged sessions that are escalated to a live
customer service representative
- The number of agent queries that cause a knowledge source error
What should you identify for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 98
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