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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Build and extend agents in Copilot Studio | 40–45% | - Integrate with services and systems
|
| Topic 2: Test, deploy, and manage agents | 20–25% | - Test and validate agent performance
|
| Topic 3: Plan and configure agent solutions | 30–35% | - Plan an agent solution
|
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NEW QUESTION # 82
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.
Answer:
Explanation:
NEW QUESTION # 83
An organization uses separate development and production environments for agents in Copilot Studio.
You create an agent in the development environment.
The agent must be deployed to production by using an application lifecycle management (ALM) process.
You must configure the solution strategy to meet the following requirements:
* Developers must be able to continue modifying the agent in the development environment.
* The production deployment must prevent direct modification.
* The agent must be exported from development and imported into production in a deployment-ready state.
Which solution type should you use for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
An unmanaged solution is used as the development source. It allows developers to add, remove, and modify the agent and its related components as development continues. Microsoft identifies unmanaged solutions as the source for Power Platform assets and recommends using them only in development environments.
For deployment, the unmanaged development solution is exported as a managed solution . The resulting managed package is the deployment artifact imported into production. The original solution in development remains unmanaged, allowing the team to continue implementing changes and produce subsequent managed versions.
A managed solution creates a controlled solution layer in the production environment. Its components cannot be edited directly from within the managed solution, and components cannot be freely added or removed.
Managed properties can further restrict whether individual components permit customization. Organizations can also enable Block unmanaged customizations in Dataverse to prevent unapproved changes from being layered over managed production components.
The default solution and a standalone agent do not provide an appropriate controlled ALM package.
Therefore, the correct pattern is unmanaged in development # export as managed # import the managed solution into production .
Relevant study-guide area: Test and manage agents # Implement application lifecycle management for agents in Copilot Studio # Create a solution and add existing agents to a solution . See Solution concepts and Export and import agents using solutions .
NEW QUESTION # 84
A company is building a Copilot Studio solution that must answer policy questions, retrieve operational metrics from structured data, and execute authenticated updates in backend systems. The company decides to use multiple specialized agents rather than a single monolithic agent. Custom agents for each capability already exist.
To meet the business requirements, the solution must:
* Retrieve up-to-date metrics from structured data.
* Answer policy questions by using curated knowledge.
* Execute authenticated updates during conversations.
You need to select the appropriate agent design for each requirement.
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.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Requirement
Configuration
The solution must retrieve up-to-date metrics from structured data.
Delegate the request to a data-access agent.
The solution must answer policy questions by using curated data sources.
Delegate the request to a knowledge-focused agent.
The solution must execute authenticated updates during conversations.
Delegate the request to a tools-first agent.
A data-access agent is appropriate for operational metrics because it specializes in retrieving current information from structured systems such as Microsoft Fabric, Dataverse, SQL databases, or real-time Power Platform connector sources. Its scope and instructions can be optimized for analytical queries and structured data retrieval.
A knowledge-focused agent is designed around authoritative, curated content such as policy documents, SharePoint libraries, approved websites, and enterprise knowledge bases. Its instructions and knowledge- source descriptions can constrain responses to approved material, making it the appropriate specialist for policy questions.
A tools-first agent is the correct design for authenticated updates. Tools-including connectors, agent flows, REST APIs, custom connectors, and MCP tools-allow an agent to interact with backend systems and perform transactional operations. Connections can use the appropriate user or service authentication strategy, while tool inputs and outputs control the requested update.
Delegating all three responsibilities to a single agent would recreate the monolithic design that the company explicitly wants to avoid. Separating the capabilities improves modularity, privilege isolation, independent maintenance, and orchestration accuracy. The primary agent should use clear connected-agent descriptions so it can select the appropriate specialist at runtime. Relevant study-guide topics are Design multi-agent solutions , Configure multi-agent collaboration , Connect to enterprise knowledge sources , and Add tools to agents . See Multi-agent orchestration patterns and Add tools to custom agents .
NEW QUESTION # 85
You deploy an agent in Microsoft Copilot Studio that triggers a cloud flow to update customer records in Microsoft Dataverse.
You observe the following after deploying the agent:
* Several runs fail because of a temporary authentication issue.
* The flow continues to trigger and generate additional failed runs.
You must:
* Stop additional failed executions while troubleshooting.
* Process a failed execution after resolving the authentication issue.
* Validate whether the most recent executions succeeded immediately after resolution.
You need to use the appropriate monitoring and management actions to restore normal flow operation and validate successful execution.
Which action should you perform for each requirement?
Answer:
Explanation:
Explanation:
Requirement
Correct action
Stop additional failed executions while troubleshooting.
Disable the flow.
Process a failed execution after resolving the authentication issue.
Resubmit the run.
Validate whether the most recent executions succeeded immediately after resolution.
Review the Activity history.
Disabling the flow is the appropriate containment action because it prevents the trigger from creating additional executions while the authentication connection is investigated and repaired. Cancelling one run affects only that particular execution and does not prevent subsequent trigger events. Deleting the flow is unnecessarily destructive, while changing the trigger condition would alter the flow's intended business logic.
After repairing the Dataverse connection or authentication credentials, the failed execution should be resubmitted . Resubmission processes the previous run again using its recorded trigger inputs, allowing the corrected connection to complete the actions without manually recreating the original event. Microsoft permits failed flow runs to be selected and resubmitted from their run history.
The Activity history is the appropriate place to perform immediate validation. It shows individual flow executions, their status, duration, and action-level details. By contrast, Analytics presents aggregated performance information and may not update immediately; Microsoft notes that run-related analytics visualizations can take up to 30 minutes to reflect activity.
Relevant study-guide area: Plan and configure agent solutions # Create and monitor agent flows in Copilot Studio # Monitor agent flows and implement error handling . See Monitor your agent flows and Cancel or resubmit flow runs .
NEW QUESTION # 86
A company uses an agent in Copilot Studio that produces structured responses using a reusable instruction template. The responses follow the required tone and format. During a compliance review, the team discovers that the responses were generated using a model that is not on the approved list.
The company requires that the reusable instruction template continues to be used and the model used for prompt-driven responses meets governance requirements.
The solution must ensure that prompt-driven responses are generated using an approved model.
You need to configure the solution that meets the requirements.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,E
Explanation:
[A]
You must publish the agent to ensure that the updated model settings take effect and that all prompt-driven responses automatically transition to using the approved model across your connected channels.
Changing the generative AI model in Microsoft Copilot Studio modifies the configuration in the authoring canvas, but it does not instantly apply to live users. Publishing the agent pushes these metadata changes to production, ensuring data compliance and governance standards are met.
[E]
To ensure that prompt-driven responses are generated using an approved model while continuing to use your reusable instruction template, you must ensure the reusable instruction template is the one applied to the agent that generates the structured response (specifically by selecting the compliant model within the Copilot Studio agent's settings where the template is run).
Incorrect:
[Not C]
Large Language Models (LLMs) cannot see or change the infrastructure they are running on.
The reality: Adding a text instruction like "Use GPT-4" into a prompt template does not physically change the underlying model being utilized by Copilot Studio. If the agent is configured to use an unapproved model, it will continue to use that model to read your new instruction.
[Not D]
Grounding sources provide factual data, not model governance.
Azure AI Search acts as a Retrieval-Augmented Generation (RAG) data source to prevent hallucinations and provide internal documents to the agent. It does not dictate or enforce which underlying LLM orchestrates and generates the final response text.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/prompt-model-settings
NEW QUESTION # 87
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