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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design AI-powered business solutions | 25–30% | - Design extensibility and integration
|
| Topic 2: Plan AI-powered business solutions | 25–30% | - Analyze requirements for AI-powered business solutions
|
| Topic 3: Deploy AI-powered business solutions | 40–45% | - Monitor, analyze, and tune solutions
|
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NEW QUESTION # 102
A company has a Microsoft Dynamics 365 Sales environment that has Microsoft Copilot enabled.
You need to customize Copilot by tailoring how opportunity summaries are generated or how they are presented to users.
Solution: You add the opportunity summary widget to the Opportunity form. Does this meet the goal?
Answer: A
Explanation:
Adding the opportunity summary widget to the Opportunity form can make the summary visible in the user interface, but it does not tailor how the summary is generated, nor does it meaningfully customize its presentation logic beyond placement.
The question asks whether this meets the goal of customizing Copilot by tailoring:
* how opportunity summaries are generated, or
* how they are presented to users
Simply placing the widget on the form is more of a UI inclusion step than a true customization of Copilot summary behavior or rendering logic.
NEW QUESTION # 103
A company uses Microsoft 365 and Dynamics 365
You need to recommend a solution to automatically summarize email threads, generate suggested replies in Microsoft Outlook, and provide meeting preparation summaries that include relevant customer relationship management (CRM) data.
Solution: You recommend a classic Microsoft Dataverse workflow.
Does this meet the goal?
Answer: A
NEW QUESTION # 104
A company uses multiple Microsoft Copilot Studio agents across different channels.
You need to recommend a monitoring solution that provides comprehensive telemetry data and performance insights for the agents.
What should you include in the recommendation?
Answer: C
Explanation:
Azure Application Insights (part of Azure Monitor) is the best Microsoft solution for monitoring comprehensive telemetry and performance for Copilot Studio agents across multiple channels.
While Copilot Studio has built-in analytics, Application Insights allows you to centralize data from many different agents and channels into one unified view.
Why Use Azure Application Insights?
Connecting your agents to Application Insights provides deeper technical and operational visibility that the standard dashboard lacks:
Centralized Monitoring: View telemetry for all your agents in a single Azure resource rather than checking each agent individually in Copilot Studio.
Custom Kusto Queries: Use KQL (Kusto Query Language) to run complex analyses, such as identifying common errors, measuring specific topic latency, or tracking custom events.
Real-Time Alerts: Set up proactive alerts to notify you if an agent has a high error rate, spike in latency, or fails to respond.
Enhanced Transcripts: Capture detailed message-level logs, including user prompts and agent responses, to troubleshoot specific conversation failures.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/analytics-overview
NEW QUESTION # 105
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: C
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 # 106
Drag and Drop Question
A company has a Microsoft Foundry project that uses a single agent and a single prompt to complete a series of tasks.
The agent encounters the following issues:
- It frequently produces incomplete results.
- It struggles with domain-specific reasoning.
- Agent response times are remarkably slow.
You need to recommend a solution to improve the overall performance and accuracy of the agent.
What should you include in the recommendation? To answer, drag the appropriate actions to the correct requirements. Each action may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Move to a multi-agent architecture
To improve performance
Moving to a multi-agent architecture in Azure AI Foundry is a highly effective strategy to overcome performance bottlenecks, as single-agent systems often struggle with long-running tasks, leading to high latency and timeout issues. By decomposing complex tasks into smaller, specialized subtasks, you can improve response times through parallel processing and targeted tool usage.
Incorrect:
* Upgrade to a larger generative AI model
To address slow response times in your Microsoft Foundry agent, upgrading to a larger generative AI model is one option, but it may increase latency in some scenarios due to higher processing demands. Instead, a combination of prompt optimization, model selection, and architectural changes in Microsoft Foundry is recommended to improve performance.
Box 2: Add a grounding data source
To improve accuracy
To improve the performance of an agent in a Microsoft Foundry project experiencing incomplete results, weak domain reasoning, and high latency, adding a grounding data source is a highly effective strategy. Grounding connects the Large Language Model (LLM) to verified external data, ensuring responses are accurate, contextual, and less likely to hallucinate.
Reference:
https://developer.microsoft.com/blog/designing-multi-agent-intelligence
https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/ground-your-ai-agents-with- knowledge-from-bing-search-microsoft-fabric-sharepoin/4303634
NEW QUESTION # 107
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