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| Section | Objectives |
|---|---|
| Plan and Design Copilot Studio AI Agents | - Selecting Copilot Studio capabilities and architecture approach - Requirements analysis for AI agent solutions - Designing conversation flows and agent behavior |
| Build and Configure AI Agents in Copilot Studio | - Creating and configuring copilots and topics - Configuring actions, plugins, and connectors - Using generative AI and prompt engineering in Copilot Studio |
| Deploy, Monitor, and Optimize AI Agents | - Publishing and deploying copilots - Monitoring performance and analytics - Iterative improvement and lifecycle management |
| Integrate External Systems and Data Sources | - Using Power Automate for workflow orchestration - Integrating Dataverse and Power Platform components - Connecting Azure services and APIs |
| Governance, Security, and Compliance | - Managing access control and authentication - Data protection and responsible AI practices - Environment management in Power Platform |
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問題 #50
You run multiple evaluation tests for an agent in Copilot Studio before expanding user access.
You observe the following about the evaluation results:
Each evaluation case is reported as meeting or missing the expected response.
Some evaluation cases fail repeatedly across several runs.
Each run displays the expected response and the response generated by the agent.
You need to interpret what the evaluation results reveal.
What should you conclude based on each interpretation? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
問題 #51
You need to ensure that performance-related questions are handled in accordance with Fabrikam Inc.'s defined architecture and business requirements Which capability should Operations Concierge use?
答案：D
解題說明：
Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: A Fabric Data Agent is the capability designed for questions that must be answered from governed analytical data. Fabrikam's backlog, SLA-risk, parts-shortage, and dispatch-delay measures already reside behind a Fabric semantic model, so delegating these requests to the data agent preserves the business definitions, relationships, and security applied in Fabric. The result can be returned in a structured form suitable for weekly operational review rather than being estimated from general model knowledge. A Foundry agent is already assigned to summarization and classification of long-form text; it is not the authoritative source for operational KPIs. An MCP server is appropriate when the user must perform a transaction, such as creating a parts request, but the question asks for performance information rather than an update. Azure AI Search supports document retrieval for policies and procedures, not calculation over the Fabric semantic model. The primary agent's description for the Fabric specialist should explicitly mention the supported metrics so generative orchestration consistently delegates those requests instead of attempting an unsupported generative answer. Study Guide alignment: Integrate and extend agents in Copilot Studio > Configure multi-agent collaboration from Copilot Studio > Integrate a Fabric data agent.
問題 #52
Drag and Drop Question
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 action already exist.
To meet the business needs, the solution must meet the following requirements:
- The solution must retrieve up to date metrics from structured data.
- The solution must answer policy questions using curated knowledge.
- The solution must execute authenticated updates during conversations.
You need to select the appropriate agent design for each requirement.
Which approach 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. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
Box 1: Delegate the request to a tools-first agent
To retrieve up-to-date operational metrics from structured data in a multi-agent Copilot Studio architecture, you should configure the parent system to delegate the request to a tools-first agent.
Specialized Actions: In Copilot Studio, custom connectors and APIs that query or update structured data are configured as tools.
Dynamic Execution: A tools-first agent is specifically designed to evaluate the user's intent, select the correct authenticated API/connector (the tool), and pass parameters to retrieve real-time data.
Delegation Overlap: Knowledge-focused agents are optimized for RAG (retrieval-augmented generation) over static, unstructured files/documents rather than dynamic operational metrics.
Box 2: Delegate the request to a knowledge-focused agent.
To meet the requirement of answering policy questions using curated knowledge within a multi- agent Copilot Studio solution, you should delegate the request to a knowledge-focused agent.
Box 3: Delegate the request to a tools-first agent
To execute authenticated updates and actions in backend systems, you should delegate the request to a tools-first agent in Copilot Studio. While other specialized agents handle document synthesis or data analysis, a tools-first agent is configured to process API-based actions and plugins with proper end-user authentication.
In multi-agent architecture within Microsoft Copilot Studio, agents are typically categorized by their core operational focus. A tools-first agent is explicitly designed to prioritize taking specific actions-such as executing backend APIs, running plugins, or triggering workflows-making it the designated agent type for processing transactional and authenticated system updates.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/guidance/multi-agent-patterns
問題 #53
An agent in Copilot Studio is evaluated using a fixed test set and an automated evaluation method.
After running the evaluation, the team observes that the same three interactions fail in every run, while all other interactions pass consistently.
You need to use the evaluation results to determine an accurate conclusion.
What should you conclude from the evaluation results?
答案：C
問題 #54
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
A team is implementing ALM for agents in Copilot Studio across multiple environments.
The team requires a consistent way to manage and transport agent assets.
You need to recommend a solution that supports creation and use of ALM scenarios for agents in Copilot Studio.
Solution: Publishing an agent to a channel is the required ALM container for transporting agents between environments.
Does the solution meet the goal?
答案：A
解題說明：
Correct:
* Builders can change environment variable values directly in Copilot Studio to complete ALM configuration without administrative tools.
builders can change environment variable values directly during the solution import process to complete Application Lifecycle Management (ALM) configurations.
Native ALM in Copilot StudioMicrosoft Copilot Studio embeds native solution management capabilities directly within the app authoring environment. This design minimizes the reliance on standalone administrative portals or complex external tools for standard deployment tasks.
The Import Process: When you transport your agent by importing its containing solution into a target downstream environment (such as Test or Production), Copilot Studio prompts you for environment-specific parameters.
Modifying Values: Builders can update the "current value" of environment variables right inside the import wizard panel. Alternatively, you can let them fall back to pre-defined default parameters.
Supported Scenarios: This inline editing mechanism is commonly used to dynamically update variables tied to external resources, such as changing SharePoint URLs or public website links used for generative AI knowledge grounding between sandbox and live states.
Incorrect:
* Publishing an agent to a channel is the required ALM container for transporting agents between environments.
* Publishing an agent to a channel is not the required or correct ALM container for transporting agents between environments.
* To export, import, and manage agents between environments, you need to create and use a custom solution.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/guidance/alm
問題 #55
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AB-620考試資料: https://www.vcesoft.com/AB-620-pdf.html