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| Section | Objectives |
|---|---|
| Topic 1: Build and Configure AI Agents in Copilot Studio | - Using generative AI and prompt engineering in Copilot Studio - Configuring actions, plugins, and connectors - Creating and configuring copilots and topics |
| Topic 2: Governance, Security, and Compliance | - Environment management in Power Platform - Managing access control and authentication - Data protection and responsible AI practices |
| Topic 3: Deploy, Monitor, and Optimize AI Agents | - Publishing and deploying copilots - Iterative improvement and lifecycle management - Monitoring performance and analytics |
| Topic 4: Integrate External Systems and Data Sources | - Integrating Dataverse and Power Platform components - Using Power Automate for workflow orchestration - Connecting Azure services and APIs |
| Topic 5: Plan and Design Copilot Studio AI Agents | - Designing conversation flows and agent behavior - Requirements analysis for AI agent solutions - Selecting Copilot Studio capabilities and architecture approach |
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NEW QUESTION # 13
Drag and Drop Question
A team is preparing to assess an agent in Copilot Studio before expanding access to additional users.
The team requires an evaluation that provides controlled, repeatable, and consistently measured test runs.
The evaluation must:
- Be triggered directly by the team to control when testing occurs.
- Determine success based on clearly established acceptance criteria.
- Rely on a predefined set of inputs that ensures consistency across
repeated runs.
You need to configure the evaluation to meet the requirements
What should you configure? To answer, move the appropriate configurations to the correct evaluation 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: Execute the evaluation on demand
To trigger structured, repeatable agent evaluations manually inside Microsoft Copilot Studio so your team can precisely control when testing occurs, you should click Evaluate directly from the Evaluate tab within the Copilot Studio user interface.
Selecting this option initiates an on-demand, interactive test run against your defined test sets.
Box 2: Compare responses against defined expectations
Comparing generated responses against defined expectations is exactly how you establish pass/fail signals. Microsoft Copilot Studio automates this process so you can achieve controlled, repeatable, and consistently measured tests before expanding access.
Box 3: Use a curated prompt list as the interaction source.
Using a curated prompt list-known as an evaluation test set-is exactly how you ensure consistency. It removes subjective, one-off testing and establishes a structured framework for measuring response quality.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/analytics-agent-evaluation-overview
NEW QUESTION # 14
You need to evaluate whether the current configuration decisions for the Blue Yonder Copilot agent comply with the company's security and governance policies.
Which compliance status should you assign to each configuration decision? To answer, move the appropriate compliance statuses to the correct configuration decisions. You may use each compliance status 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 # 15
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 # 16
You are testing an agent in a development environment and need to verify how it responds across multiple conversation paths before publishing to production. What should you use?
Answer: A
Explanation:
The built-in Test panel lets builders simulate conversations, inspect variable values, and trace which topics/nodes fired, without affecting the published production agent.
NEW QUESTION # 17
A company is building an agent in Copilot Studio.
The agent must meet the following requirements:
Responses must be grounded in approved internal content.
The agent must be able to retrieve information from configured data sources in a topic.
You need to configure the agent so that topic responses are grounded from custom data sources.
What should you configure?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: A generative answers node inside the topic is the correct place to retrieve from configured knowledge sources for that specific conversational path. The node can use the user ' s message as input, search designated sources, and generate a grounded response from the retrieved content. Topic-level sources can override the broader agent-level set, which is useful when the topic must restrict itself to approved data. A custom prompt that merely instructs the model to use company information does not create or enforce a retrieval connection. Increasing the response limit affects length, not grounding. Analytics measures usage after the fact and cannot supply knowledge.
Adding SharePoint only at agent level is unnecessarily narrow and would not satisfy the requirement for custom sources configured within the topic. The builder should enable Search only selected sources when strict scoping is required and turn off general knowledge if no ungrounded fallback is allowed. Testing should inspect both the answer and the resource details to confirm that the intended source supplied the evidence.
Study Guide alignment: Plan and configure agent solutions > Configure topics > Configure generative answers node; Configure advanced agent responses with custom knowledge sources.
NEW QUESTION # 18
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