A team is developing an agent in Copilot Studio and wants to evaluate its behavior before expanding access.
The team needs a repeatable, consistent method to test the agent using predefined interactions.
You need to ensure that agent responses can be evaluated consistently across multiple test runs.
What should you do?

• A. Define test sets of prompts and expectations.
• B. Change the evaluation method to a qualitative approach.
• C. Publish the agent to additional channels for broader usage.
• D. Enable telemetry and analytics to monitor intent matches, fallback usage, and response frequency.
• E. Review evaluation scores from previous production sessions.

A company requires an agent in Copilot Studio to answer questions using content stored in a third-party knowledge base system and reuse that content for knowledge grounding.
The agent must ground answers using indexed enterprise knowledge from a non-Microsoft system.
You need to choose the correct integration approach.
What should you use?

• A. Add a Copilot connector knowledge source for authorized content.
• B. Add a Copilot Studio tool as a real-time knowledge source.
• C. Call the third-party API from a topic for each question.
• D. Add a Copilot Studio tool and call it from a topic.

Hotspot Question
You are setting up an evaluation for an agent in Copilot Studio before expanding access to more users.
You must configure the evaluation so that the agent is tested by using prepared interactions rather than impromptu conversations.
The evaluation should provide the following functionality for users:
- An administrator updates a sensitive configuration value. The
administrator requires the change to take effect at runtime without
republishing the agent.
- A builder needs to confirm a configuration value but must not be able to modify it from Copilot Studio.
- An administrator updates a non-secret configuration value and
requires the updated value to be used by the published agent at
runtime.
You need to create a test set-based evaluation that supports consistent test runs for each scenario.
What should you configure for each scenario? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Case Study 2 - Fabrikam Inc.
Background
Fabrikam Inc. is a Canada-based manufacturer with a growing service organization that supports field technicians and internal operations teams. Fabrikam Inc. plans to launch a new internal agent solution named Operations Concierge to reduce time spent searching policy content, retrieving operational metrics, and executing routine transactions.
The agent will be used by three groups:
- Service coordinators who triage incoming service requests
- Field technicians who need guided procedures and parts availability
- Operations managers who monitor KPIs and exceptions
The agent solution must work in real-world operational conditions. Users often ask questions mid- call with a customer or while coordinating parts shipments. The agents require quick, reliable outcomes. As a result, Fabrikam Inc. requires the solution to:
- Provide grounded answers with traceability when it provides guidance.
- Retrieve real-time metrics when users ask for operational status.
- Execute authenticated updates when users initiate a flow (such as creating a parts request).
Fabrikam Inc. also expects the solution to be maintained by multiple makers and developers across the year. The company has experienced duplicated logic and inconsistent behavior across different agents. This project emphasizes reuse, governance, and maintainability across teams.
Current environment
Fabrikam Inc. runs three Microsoft Power Platform environments for agent development and release: Dev, Test, and Prod.
The team plans to build the agent and validate it in Dev and Test, then promote to Prod by using a controlled release process that supports repeatable deployments.
Fabrikam Inc. already has two assets the team wants to reuse:
- A partially completed Copilot Studio agent named Service Desk Agent, used by IT to create internal tickets and route requests
- A Microsoft Foundry agent created by a central AI team that performs specialized summarization and classification for long-form text (for example, summarizing call transcripts into an incident narrative) Fabrikam Inc. also has operational and knowledge data sources:
- A curated policy library (internal SOPs, service warranty rules, escalation criteria, and standard operating procedures)
- A set of indexed documents and procedures in an Azure AI Search service that supports vector search for the policy library
- A Microsoft Fabric workspace that includes a semantic model used by operations leadership for reporting Business requirements Fabrikam Inc. requires Operations Concierge to meet the following business requirements:
- Traceability requirement: When the agent provides policy guidance or procedural recommendations, users must be able to see where the answer came from.
- Metrics requirement: When users ask about service performance (backlog, SLA risk, parts shortages, dispatch delays), the solution must return up-to-date metrics in a structured format that operations managers can use in weekly reviews.
- Transaction requirement: The solution must support authenticated updates initiated during conversations, including creating a parts request and updating a service case status.
In addition, Fabrikam Inc. wants to avoid duplicating common assets across agents:
- The team must reuse the same set of escalation topics, MCP tool definitions, and a standard safety disclaimer across three different agents.
- Only the platform engineering group as allowed to edit shared assets. However, all agent authors must be able to use them.
Technical requirements
The Fabrikam Inc. solution architecture uses a multi-agent approach so that specialist responsibilities are isolated and can evolve independently.
The Operations Concierge (primary agent) must coordinate the following specialist capabilities:
- Policy and procedure Q&A: Use an enterprise knowledge source that supports indexed retrieval across the curated policy library and service procedures.
- Operational metrics: Delegate metric queries to a Fabric Data Agent that reads governed business data through the Fabric semantic model.
- Authenticated updates: Use tools exposed by an existing internal Model Context Protocol (MCP) server that provides transactional operations for the service organization.
- Specialized processing: Delegate summarization and classification requests to an existing Microsoft Foundry agent.
Fabrikam Inc. will onboard two MCP servers as tools:
- PartsOps MCP server: exposes tools for parts availability checks and parts request creation.
The server requires per-user authentication because actions must be traceable to the requesting user.
- WarrantyRules MCP server: exposes a read-only tool for validating warranty coverage. The server uses an API key shared by the agent team.
Fabrikam Inc. has also defined a collaboration requirement with the existing Service Desk Agent:
- The primary agent must delegate IT-specific requests to the existing Service Desk Agent rather than reimplement ticket creation logic.
Finally, Fabrikarn Inc. plans to support a partner integration:
- For shipment tracking inquiries, Fabrikam Inc. will delegate to a partner-provided agent that is only available through a standardized agent-to-agent endpoint.
Issues and constraints
During early testing, Fabrikam Inc. found three recurring problems:
- Makers are copying and modifying the same components across agents, resulting in inconsistent disclaimers and duplicated tools.
- Users can obtain a correct answer, but the response is not consistently traceable to a source when the agent uses knowledge.
- The primary agent can route some requests, but specialist capabilities are not consistently delegated (for example, some metric questions are answered generatively instead of being routed to the Fabric Data Agent).
You are part of the engineering team responsible for correcting the design and configuration to meet the preceding requirements and constraints.
You need to configure Operations Concierge so that prompt-driven summarization behavior aligns with Fabrikam Inc.'s architectural requirements.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

• A. Select a model from the Foundry model catalog in the custom prompt configuration.
• B. Configure Azure AI Search as the grounding source for summarization prompts.
• C. Enable citations to control how summarization output is generated.
• D. Publish the agent to ensure the selected model is enforced for summarization.
• E. Create a reusable custom prompt template and apply it where summarization responses are generated.

Drag and Drop Question
A company has an Azure AI Search vector index that contains enterprise content and wants to use it as a knowledge source for an agent in Copilot Studio.
To meet business needs, the knowledge source must meet the following requirements:
- The agent must connect to the Azure AI Search service.
- The agent must use a supported authentication method for the
connection.
- The agent must ensure answers are grounded using the specified Azure
AI Search index.
You need to connect to Azure AI Search as a knowledge source for 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.