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| Section | Objectives |
|---|---|
| Governance, Security, and Compliance | - Data protection and responsible AI practices - Environment management in Power Platform - Managing access control and authentication |
| Integrate External Systems and Data Sources | - Connecting Azure services and APIs - Integrating Dataverse and Power Platform components - Using Power Automate for workflow orchestration |
| Build and Configure AI Agents in Copilot Studio | - Using generative AI and prompt engineering in Copilot Studio - Creating and configuring copilots and topics - Configuring actions, plugins, and connectors |
| Deploy, Monitor, and Optimize AI Agents | - Publishing and deploying copilots - Iterative improvement and lifecycle management - Monitoring performance and analytics |
| Plan and Design Copilot Studio AI Agents | - Selecting Copilot Studio capabilities and architecture approach - Designing conversation flows and agent behavior - Requirements analysis for AI agent solutions |
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NEW QUESTION # 64
A company has an approved custom connector that uses a REST API for an internal system.
An agent in Copilot Studio must call this connector during conversations to retrieve or update data.
To meet the business needs, the solution must meet the following requirements:
Make the connector actions available for the agent to invoke.
Ensure authentication is handled at the service level, not per user.
Pass conversation context into the connector when it is called.
Surface the returned data of the connector to the user in the conversation.
You need to configure the agent and the connector action 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 # 65
A company must enable an agent in Copilot Studio to execute operations exposed by an external MCP server.
The MCP server publishes callable tools and requires authentication.
The agent must be able to invoke MCP tools during conversations.
You need to configure MCP tools so the agent can execute MCP tool calls.
What should you do?
Answer: C
NEW QUESTION # 66
A company is preparing an agent flow so that it can be invoked by an agent during conversations.
The agent flow must meet the following requirements:
* The agent must be able to trigger the flow.
* The agent flow must be verified.
You need to prepare an agent flow so that it can be used by the agent.
In which order should you perform the actions to prepare the agent flow? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
* Create a new agent flow artifact in the environment.
* Configure the invocation trigger that allows the agent to call the agent flow.
* Configure the actions that implement the required behavior.
* Validate the agent flow behavior with representative test runs.
* Make the agent flow available for agent usage.
An agent flow must first exist in the target environment before its trigger or processing logic can be configured. The invocation trigger is then configured so Copilot Studio can initiate the flow during a conversation. For an agent-callable flow, this is typically the When an agent calls the flow trigger. The flow should also return any required results through the corresponding response action.
After establishing the trigger, the author adds the actions that implement the required business process, such as retrieving records, calling connectors, applying conditions, or updating enterprise systems. Representative test runs should then be performed to confirm that inputs are accepted correctly, actions execute successfully, errors are handled appropriately, and expected outputs are returned to the agent.
Once the flow has been verified, it can be published and made available to the agent as a tool. Exposing an incomplete or unverified flow prematurely could cause conversation failures or incorrect tool results.
Relevant study-guide area: Plan and configure agent solutions # Create and monitor agent flows in Copilot Studio # Create an agent flow . See Create an agent flow as a tool and Call an agent flow from an agent .
NEW QUESTION # 67
Hotspot Question
A team deploys Copilot Studio solutions by using Power Platform Pipelines.
You must configure a deployment strategy that meets the following requirements:
- Ensure that the solutions deploy in dev, test, and production
environments in order.
- Identify missing connection references or environment variables
before the deployment process begins.
- Manage all pipeline stages and security from a single, unified
location for all linked environments.
You need to configure the pipeline to meet the requirements.
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: Sequential stage progression
Power Platform Pipelines stages are ordered, and each stage can specify its predecessor. A solution must be successfully deployed to the preceding stage before it becomes eligible for the next, which enforces dev → test → production ordering. Direct target selection would bypass that ordering entirely.
Box 2: Deployment input validation
Before the deployment runs, the pipeline validates the solution against the target environment and surfaces missing connection references, environment variable values, and other required inputs so they can be supplied up front. Managed solution deployment is just the packaging type; it doesn't perform pre-checks.
Box 3: Centralized pipeline configuration
The host (pipelines) environment holds the Deployment Pipeline Configuration app, where all stages and their security roles are defined for every linked development environment.
Environment-level or maker-defined settings would fragment that administration.
Reference:
https://learn.microsoft.com/en-us/power-platform/alm/run-pipeline
https://learn.microsoft.com/en-us/power-platform/alm/custom-host-pipelines
https://learn.microsoft.com/en-us/power-platform/alm/extend-pipelines
https://learn.microsoft.com/en-us/power-apps/maker/data-platform/environmentvariables
https://www.microsoft.com/en-us/power-platform/blog/power-apps/environment-variables-are-now-always-visible-and-editable-during-solution-import-and-pipeline-deployments/
NEW QUESTION # 68
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 # 69
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