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| Section | Weight | Objectives |
|---|---|---|
| Test, deploy, and manage agents | 20–25% | - Test and validate agent performance
|
| Build and extend agents in Copilot Studio | 40–45% | - Implement advanced capabilities
|
| Plan and configure agent solutions | 30–35% | - Plan an agent solution
|
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NEW QUESTION # 63
You are configuring an agent in Copilot Studio for an organization. The organization uses Microsoft Dataverse, Dynamics 365, and SharePoint Online.
The agent must retrieve information from enterprise systems and internal documents. The agent must meet the following requirements:
* Must retrieve the current balance of a customer account stored in Dataverse.
* Must retrieve the real-time shipping status of an order that updates throughout the day in Dynamics 365.
* Must answer questions based on the content of a static PDF policy document.
* Must provide a welcome message that does NOT depend on enterprise data.
Answer:
Explanation:
NEW QUESTION # 64
A company must enable an agent in Copilot Studio to execute operations exposed by an external MCP server.
The MCP server publishes callable tools and requires authentication.
The agent must be able to invoke MCP tools during conversations.
You need to configure MCP tools so the agent can execute MCP tool calls.
What should you do?
Answer: C
Explanation:
You should add the Model Context Protocol (MCP) server directly to the agent's tool configuration in Microsoft Copilot Studio. Doing this automatically registers the published tools and sets up the underlying runtime bridge.
To enable a Copilot Studio agent to execute tools from an authenticated external Model Context Protocol (MCP) server, use the Microsoft Copilot Studio onboarding wizard to add the MCP server as a tool, supply the server endpoint URL, and configure your required authentication parameters Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/mcp-add-existing-server-to-agent
NEW QUESTION # 65
Two Copilot Studio agents need to delegate tasks to each other directly, with each agent able to advertise its own capabilities to the other. Which two components are required? (Choose two.)
Answer: A,D
Explanation:
The Agent2Agent (A2A) protocol relies on an agent card (capability discovery) and an exposed A2A endpoint so one agent can securely discover and invoke another agent's capabilities.
NEW QUESTION # 66
Drag and Drop Question
A company is integrating an agent in Copilot Studio into a custom application by using the Microsoft 365 Agents SDK.
The company has the following requirements for the application:
- The deployment workflow must follow a dependency driven sequence.
- The agent must be published before configuration values are
retrieved.
- End-to-end validation must occur before production deployment.
You need to plan the deployment workflow to integrate the agent.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
Step 1: Publish the agent
Publishing is the foundational action that makes the agent's build available for use. In Copilot Studio, an agent must be published before it can be connected to any channel or before its connection metadata (endpoint URL, App ID, client secret, tenant ID) becomes accessible. This is explicitly stated in the scenario as a hard requirement - configuration values cannot be retrieved before publishing. Publishing essentially "activates" the agent so downstream systems have something concrete to reference.
Step 2: Retrieve the current agent configuration values
Once the agent is published, its configuration details become available and can be retrieved.
These values (App ID, App password/secret, tenant ID, and the agent's endpoint) are what allow an external application to authenticate and communicate with the Copilot Studio agent. This step is dependent on Step 1 by definition - there is nothing to retrieve until the agent exists in its published state. This satisfies the requirement that publishing precede configuration retrieval.
Step 3: Implement the Microsoft 365 Agents SDK in the app using the agent configuration With the configuration values in hand, the development team wires the Microsoft 365 Agents SDK into the custom application. The SDK uses the retrieved App ID, secret, and endpoint to establish the connection between the custom app and the published Copilot Studio agent. This step logically depends on Step 2 - you can't configure the SDK with values you haven't yet retrieved.
This is the actual "integration" work described in the scenario's premise.
Step 4: Validate the end-to-end conversation workflow in a test environment After the SDK integration is implemented, the full conversation flow - from the custom app, through the SDK, to the Copilot Studio agent, and back - must be tested in a non-production environment. This satisfies the explicit requirement that end-to-end validation occur before production deployment. Since production deployment isn't one of the four listed actions in this trimmed variant, this validation step is the final, gating checkpoint of the visible workflow - implying that production deployment (not shown as a draggable option) would follow only after this validation succeeds.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/guidance/channels
https://learn.microsoft.com/en-us/microsoft-copilot-studio/publication-integrate-web-or-native-app-m365-agents-sdk
NEW QUESTION # 67
Drag and Drop Question
A company uses an agent in Copilot Studio to answer questions based on enterprise content.
The content is indexed in Azure AI Search. Responses must be generated by a Microsoft Foundry model.
To meet the business needs, the solution must meet the following requirements:
- Users are able to trace each answer back to its source.
- The agent retrieves information from a centralized data index.
- Responses are generated by an enterprise-approved foundation model.
You need to configure generative answers for the agent.
What should you configure for each requirement? To answer, move the appropriate configurations to the correct requirements. You may use each configuration once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Instruct the agent to cite sources in knowledge settings
The specific behavior of how the agent presents that information-such as enforcing citations and tracing links back to the source documents-is controlled by the instructions and configurations within the agent's knowledge and conversation settings. You must explicitly configure the agent's prompt instructions or system settings to format the output with source citations so users can trace the answers.
Box 2: Configure Azure AI Search as the grounding data source
By adding Azure AI Search as a knowledge source Microsoft Copilot Studio enables the Retrieval-Augmented Generation (RAG) pattern. This ensures your generative AI model retrieves specific enterprise content from your centralized index and uses it to ground responses.
Box 3: Deploy a model in Foundry to an agent and connect to Copilot Studio Model Control: To strictly guarantee that an enterprise-approved foundation model (such as a specific Microsoft Foundry model) generates the response, you must manually deploy that specific model and link it to your agent.
Connection Workflow: Deploying the custom model in Azure AI Studio (formerly encompassing parts of Azure AI Foundry) and connecting it to Copilot Studio ensures the orchestration layer routes prompts directly to your certified LLM.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/ai-search
NEW QUESTION # 68
......
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