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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Integrate and Extend Agents in Copilot Studio | 40-45% | - Integrate agents with enterprise systems
|
| Topic 2: Test and Manage Agents | 20-25% | - Monitor and manage agent solutions
|
| Topic 3: Plan and Configure Agent Solutions | 30-35% | - Plan agent solutions
|
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NEW QUESTION # 128
A company needs an agent in Copilot Studio to complete tasks by interacting with a legacy desktop application that does not use APIs. The builder needs that UI automation can run in the supported execution context and needs a way to inspect how the automation behaves during testing conversations. to validate To meet the business needs, the solution must meet the following requirements:
* Enable the agent to perform UI automation against the legacy application.
* Inspect execution details for the automation during conversations.
You need to configure the agent based on the requirements.
Which two actions should you perform? Each correct answer presents part of the solution. Choose two.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: Computer use is the Copilot Studio capability for operating a website or desktop application's graphical interface when an API is unavailable. Enabling it gives the agent a UI automation tool driven through the supported machine execution context. During testing, the builder can open the computer-use activity or remote session from the agent's activity view to observe steps, inspect logs, and understand failures in real time. Adding a desktop flow is a different deterministic automation technology and is not required by the stated computer-use design. Publishing alone does not provision the UI automation or its machine. A custom connector cannot automate screen controls when the application exposes no API. The tool should be configured with narrowly scoped instructions, a healthy supported machine, protected credentials, and human confirmation for sensitive actions. Because computer-use behavior is probabilistic and visual interfaces change, the team should test alternate screen states, pop-ups, timing, and recovery, then monitor production sessions under strict access and retention controls. Study Guide alignment: Integrate and extend agents in Copilot Studio > Add tools to agents > Configure and monitor computer use for an agent.
NEW QUESTION # 129
An agent calls a flow that intermittently produces unexpected results during execution. The agent behaves inconsistently.
You need to investigate agent flow executions to diagnose the issue.
Which two actions should you use? Each correct answer presents a complete solution. Choose two. NOTE: Each correct selection is worth one point.
Answer: A,B
Explanation:
Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: Flow run history provides the chronological record needed to identify which executions succeeded, failed, timed out, or behaved differently. Opening the failed run details then exposes the action-level inputs, outputs, status, duration, connection errors, and failure message that explain the inconsistent result. These two views support evidence-based diagnosis without changing the flow before the cause is known. Updating the trigger, increasing a timeout, or changing connection behavior are potential remediations only after the failure pattern has been established. The Analytics summary is useful for aggregate trends but lacks the full payload and step sequence of an individual run. The Activity tab's Error column can help locate failures, but the question asks for the two complete diagnostic actions and the detailed run inspection is the more direct evidence. Reviewers should compare a failed run with a successful run using the same input, check transient versus deterministic errors, and redact or restrict access to sensitive data captured in run history. Study Guide alignment: Plan and configure agent solutions > Create and monitor agent flows in Copilot Studio > Monitor agent flows.
NEW QUESTION # 130
An agent calls a flow that intermittently produces unexpected results during execution.
The agent behaves inconsistently.
You need to investigate agent flow executions to diagnose the issue.
Which two actions should you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,F
Explanation:
[A]
To diagnose the root cause of intermittent, unexpected results and inconsistent behavior in a Copilot agent flow, the correct step is to inspect failed run details.
Selecting a specific failed run from the history allows you to view the exact inputs, outputs, and error messages for each action within that specific execution. This is the only way to pinpoint exactly why a flow intermittently produces unexpected data or behaves inconsistently during a precise moment in time.
[C]
Checking the Activity tab in Microsoft Copilot Studio is a critical diagnostic step. It lets you trace exact interactions, see the decisions the AI made, and check for specific error details to determine why the flow execution failed or behaved inconsistently.
Reference:
https://community.powerplatform.com/forums/thread/details/?threadid=09732bfd-2c63-f011-bec2-6045bdda2f4f
https://community.powerplatform.com/forums/thread/details/?threadid=ac8bc7a2-86c9-f011-bbd3-000d3a184f11
NEW QUESTION # 131
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.
Answer: B,E
Explanation:
Scenario: Specialized processing: Delegate summarization and classification requests to an existing Microsoft Foundry agent.
[C] Model Selection: Choosing a specific model from the Foundry catalog ensures that your classification and summarization requests are routed directly to the correct, optimized LLM backend within the Microsoft Foundry ecosystem.
[E] Custom Prompt Templates: Creating and applying a reusable template ensures consistent formatting, tone, and logic across all areas where your agent handles summarization and classification tasks.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/bring-your-own-model-prompts
https://learn.microsoft.com/en-us/microsoft-copilot-studio/nlu-prompt-node
NEW QUESTION # 132
Drag and Drop Question
A company is building an agent that uses an agent flow to retrieve data from an external system and return structured output.
The company needs to create a flow that the agent can invoke to process requests and return results. To support this business need, the flow must meet the following requirements:
- Accept parameters from the agent.
- Execute an action to retrieve data from the database.
- Ensure the agent receives the relevant output information.
- Validate before it is used by the agent.
You need to create a usable agent flow that the agent can invoke.
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: Create a new agent flow
Initialize the flow workspace from your platform's dashboard using a template or blank canvas.
Step 2: Define input values for the flow: Configure the flow's initial trigger parameters to accept structured data passed from the AI agent.
Step 3: Add the connector action and configure the connection
Set up the database or external system connector node to perform the data retrieval query using your defined inputs.
Step 4: Define output values for the flow
Map the retrieved system results into structured output parameters back within the response node so the agent can understand the payload.
Step 5: Test the flow run
Run a simulated diagnostic execution to validate that data transfers correctly and errors are handled before deploying it to production.
NEW QUESTION # 133
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