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| Section | Weight | Objectives |
|---|---|---|
| Plan and configure agent solutions | 30–35% | - Plan an agent solution
|
| Build and extend agents in Copilot Studio | 40–45% | - Implement advanced capabilities
|
| Test, deploy, and manage agents | 20–25% | - Deploy and manage agent lifecycle
|
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NEW QUESTION # 60
An agent in Copilot Studio must retrieve real-time data from an external system that exposes a REST API. The API returns structured JSON responses and requires authentication.
To meet the business needs, the solution must meet the following requirements:
The agent calls a REST API during a conversation.
The REST API call runs with proper authentication.
The REST API response is available to the topic at runtime.
You need to configure the REST API to meet the requirements.
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:
NEW QUESTION # 61
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.
Drag and Drop Question
You need to connect Operations Concierge to Fabrikam Inc.'s Azure AI Search knowledge index while complying with security requirements.
Which configuration should you use 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: Select the service principal as the authentication type
Ensure the Azure AI Search connection complies with the governance requirement for authenticated access.
Scenario:
A set of indexed documents and procedures in an Azure AI Search service that supports vector search for the policy library Box 2: Provide the Azure Search Endpoint URL in the connection details.
Ensure policy and procedure Q&A uses the required curated, indexed enterprise knowledge source.
Box 3: Provide the Azure Search Endpoint URL in the connection details.
Ensure the connection targets the Azure AI Search service instance that stores the indexed policy documents.
The best action to take is to Provide the Azure Search Endpoint URL in the connection details.
When connecting a Copilot agent to an external knowledge source like Azure AI Search, establishing the connection requires identifying the specific service host. Providing the Azure AI Search Endpoint URL ensures that the underlying connection infrastructure explicitly targets and authenticates against your specific Azure AI Search service instance. Once this service-level connection is validated, you are prompted in the next step to select or enter the specific index containing your documents.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/knowledge-azure-ai-search
NEW QUESTION # 62
You need to configure the agent in Copilot Studio to meet Blue Vender ' s Responsible AI and data protection requirements.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
NEW QUESTION # 63
Hotspot Question
A team is preparing to evaluate an agent in Copilot Studio before expanding access to additional users.
The team must select an evaluation method that meets the following requirements:
- Use a fixed set of prepared interactions.
- Determine responses against a predefined baseline.
- Support consistent comparison across repeated test runs.
You need to determine the evaluation method that satisfies the evaluation requirements.
Which evaluation method should you select for each requirement? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Use a curated prompt list.
The best evaluation method for this requirement is to use a curated prompt list.
Using a curated prompt list allows you to test the agent against a fixed, predefined set of prepared interactions. This method ensures consistency, as you can run the exact same test inputs before and after making updates to measure improvements or regressions accurately.
Copilot Studio supports automated testing and evaluation precisely through these types of structured test datasets.
Box 2: Compare against predefined expected outcomes.
To determine an agent's responses against a predefined baseline before expanding access, the best evaluation method is to compare against predefined expected outcomes.
Structured & Repeatable Validation: By configuring an evaluation set of conversations with exact or partial expected outcomes, you eliminate guesswork.
Targeted Graders: You can utilize different graders in the Copilot Studio Evaluation Tool (e.g., General Quality, Similarity, or Exact Match) to check if your agent's responses meet your business standards.
Box 3: Use a consistent scoring evaluation matrix
The best evaluation method to satisfy these requirements is to use a consistent scoring evaluation matrix via the built-in Agent Evaluation tool.
This method satisfies the need for repeated, reliable, and standardized comparisons across multiple testing cycles:
Automated & Structured: Agent Evaluation automatically runs up to 100 scenarios using consistent scoring mechanisms like exact text matching, semantic meaning comparison, or AI- driven quality grading.
Consistent Baselines: Every evaluation run generates a versioned, auditable scorecard, allowing you to see exactly whether changes improved or degraded the agent's performance over time.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/agents-experience/analytics-agent-evaluation-intro
NEW QUESTION # 64
An agent's generative answers responses sometimes include information not present in any configured knowledge source. What should you do to reduce this behavior?
Answer: C
Explanation:
Restricting the generative answers node (or agent instructions) to only use configured knowledge sources reduces hallucinated content that isn't grounded in approved data.
NEW QUESTION # 65
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