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| Section | Weight | Objectives |
|---|---|---|
| Plan and configure agent solutions | 30–35% | - Configure agent foundations
|
| Build and extend agents in Copilot Studio | 40–45% | - Develop agent flows and logic
|
| Test, deploy, and manage agents | 20–25% | - Deploy and manage agent lifecycle
|
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NEW QUESTION # 32
You need to ensure that every AI-generated response from the agent in Copilot Studio includes a disclaimer that complies with the company's security and governance policies.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,E
NEW QUESTION # 33
You need to integrate Fabrikam Inc.'s existing Foundry agent so Operations Concierge can delegate summarization requests Which action should you perform for each requirement? To answer, move the appropriate actions to the correct requirements. You may use each action 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 # 34
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 configure generative answers so the agent meets Fabrikam Inc's business requirements.
Which solutions should you use? To answer, move the appropriate solutions to the correct requirements. You may use each solution 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: Instructing the agent to cite sources in the knowledge settings
Traceability requirement: When the agent provides policy guidance or procedural recommendations, users must be able to see where the answer came from.
Instructing the agent to cite sources in the knowledge settings is a correct and effective action to meet this requirement.
In Microsoft Copilot Studio and Power Platform environments, configuring generative answers to include citations ensures transparency and compliance by allowing users to verify policy or procedural data directly from the original source files (such as SharePoint sites or uploaded documents).
Box 2: Configure Azure AI Search as the grounding data source
Configure Azure AI Search as the grounding data source is the best action to meet this requirement.
Centralized Indexing: Azure AI Search is explicitly designed to serve as a centralized external index. It allows you to connect Microsoft Copilot Studio agents directly to existing enterprise vector or keyword indexes rather than siloing information.Enterprise Scaling: Uploading documents directly into individual agents creates fragmented knowledge bases and hits file size limits, which breaks the requirement for a centralized index.Grounding Capabilities: Choosing this option automatically provides the agent with securely mapped data fields for retrieval, serving as the foundational knowledge source to ground the agent's generative answers.
Box 3: Select a model from the Foundry model catalog
To ensure that your Copilot agents generate responses using an enterprise-approved foundation model in Microsoft Power Platform environments, the best action is to select a model from the Foundry model catalog.
Model Selection vs. Grounding Data: The requirement explicitly specifies that the responses must be generated by an enterprise-approved foundation model.
Choosing a specific LLM from the Azure AI Foundry model catalog (or Power Platform's AI Builder models dropdown) directly controls which underlying LLM generates the text.
The alternative choices (such as uploading documents, configuring Azure AI Search, or instructing the agent to cite sources) deal exclusively with grounding data (RAG) and knowledge settings rather than selecting or restricting the actual text-generation foundation model itself.
Reference:
https://www.linkedin.com/pulse/guide-writing-effective-copilot-studio-agent-pierre-yves-delac%C3%B4te-sdcye
NEW QUESTION # 35
Hotspot Question
A company uses an agent that invokes an agent flow to exchange information during a conversation.
The company requires that the agent send data into the flow and receive structured results back from the same flow run. To support this business need, the flow must be configured to do the following:
- Capture the data provided by the agent.
- Return data results to the agent.
You need to configure the flow so that it can exchange data with the agent.
What should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Add a text input parameter
To allow a Copilot Studio agent to pass data into a flow and receive structured results back, you must configure a Power Automate flow triggered by the "Run a flow from Copilot" trigger with custom input variables, and concluding with the "Respond to Copilot" action containing structured output parameters.
Box 2: Add a text output parameter
To return structured results from an agent flow to your agent in Microsoft Copilot Studio, you should configure the "Respond to the agent" action in your flow with an output parameter.
In Microsoft Copilot Studio, when a topic or bot calls a Power Automate flow, data is sent into the flow via input parameters. To pass data, objects, or structured results back to the copilot during the exact same flow run, you must explicitly define one or more output parameters (such as a text, number, or boolean output) in the "Return value(s) to Power Virtual Agents/Copilot Studio" final step of the flow.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/advanced-use-flow
NEW QUESTION # 36
A company uses an agent flow that occasionally requires human input before continuing execution.
Some automated actions must pause until a human provides a decision or additional information. The flow must be configured to:
* Capture a human response for use in later steps.
* Continue processing within the same flow run after the response is submitted.
* Wait for a manual decision before proceeding.
You need to configure a human-in-the-loop agent flow.
Which setting should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Capture response # Use the action ' s response outputs in later steps; Continue the same run # Allow the action to wait for and return a response before continuing; Require a manual decision # Add a human approval action.
Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: A human-in-the-loop flow must preserve execution state while it waits. The approval or information action pauses the current run and returns a response when the designated person acts; triggering a new flow would lose that direct continuation contract. The response outputs-decision, comments, supplied fields, responder, and timestamps as applicable-are then referenced by downstream conditions and actions. A human approval action provides the explicit decision gate required before processing continues. Sending a notification or message alone does not wait and cannot guarantee that the decision controls the flow. The designer should configure expiration, escalation, reassignment, and rejection paths so the run cannot remain suspended without governance.
Responses should be validated before use, and sensitive values should be protected in variables and history.
Test approval, rejection, timeout, duplicate response, and unauthorized response scenarios. These controls ensure that human oversight changes execution deterministically instead of acting as an informational side channel. Study Guide alignment: Plan and configure agent solutions > Create and monitor agent flows in Copilot Studio > Create a human-in-the-loop agent flow.
NEW QUESTION # 37
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