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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Build and extend agents in Copilot Studio | 40–45% | - Implement advanced capabilities
|
| Topic 2: Plan and configure agent solutions | 30–35% | - Configure agent foundations
|
| Topic 3: Test, deploy, and manage agents | 20–25% | - Test and validate agent performance
|
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20. Frage
A company uses multiple Copilot Studio agents that perform specialized tasks.
The company needs to enable one agent to directly delegate and invoke another agent within Copilot Studio by using a standard Copilot Studio interface.
You need to configure collaboration among agents by using Copilot Studio.
What should you do?
Antwort: C
Begründung:
The correct step to take is to provide A2A (Agent-to-Agent) endpoint information for the target agent.
Standard Multi-Agent Protocol: The Agent-to-Agent (A2A) protocol is the open, standard framework specifically designed to allow independent agents to directly communicate, discover capabilities via agent cards, and delegate workflows across standard interfaces. Providing the A2A endpoint information allows the orchestrating agent to securely hand off specialized tasks to the target agent.
Incorrect:
[Not A]
Adding the target agent as a Microsoft Fabric Data Agent is a separate, specialized pattern reserved strictly for reasoning over heavy data science pipelines and querying enterprise data lakes (like Lakehouses or Semantic Models). It is not used for standard cross-framework agent collaboration or general delegation within the native Copilot Studio interface.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/agents-experience/add-agent-connected
21. Frage
You need to plan how Fabrikam Inc. will reuse shared components across multiple agents while limiting who can change the shared components in Copilot Studio Lite.
Which approach should you use for each requirement? To answer, move the appropriate components to the correct requirements. You may use each component 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
Antwort:
Begründung:
Explanation:
Prevent most builders from modifying shared assets # Share the component collection while controlling edit access; Reuse components without copying # Create a component collection and move the shared components into it; Limit use to the intended primary agent # Restrict the component collection to the primary agent.
Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: A component collection is the correct reusable unit for the escalation topics, MCP definitions, and safety disclaimer. Moving those assets into one collection creates a maintained source that multiple agents can consume without copying and later diverging. Sharing the collection with teammates while controlling edit access separates use permission from modification permission, allowing agent authors to reference the assets while reserving changes for platform engineering. If a sensitive or tightly coupled collection must be usable only by one designated primary agent, the collection can be restricted accordingly. Exporting the primary agent and copying its components would recreate the duplication problem that Fabrikam is trying to eliminate, while restricting every collection to the primary agent would prevent the required cross-agent reuse. The collection should be versioned and tested after changes because a shared update can affect several consumers. Ownership, review, and release responsibilities should also be documented so that reuse does not become an uncontrolled dependency. This design provides consistency, centralized governance, and a smaller maintenance surface across the three agents. Study Guide alignment: Plan and configure agent solutions > Plan an agent solution > Plan reusable agent components.
22. Frage
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.
An agent uses a flow that calls an external service which can occasionally fail or time out.
When a failure occurs, the agent must meet the following requirements:
- Must not terminate silently.
- Must send a notification containing the error details.
You need to configure the agent flow so that failures are handled in a controlled and predictable way.
Solution: Configure run after conditions for failure paths.
Does the solution meet the goal?
Antwort: B
Begründung:
Correct:
* Configure run after conditions for failure paths.
You should configure the "Run after" conditions in Power Automate (the underlying flow engine) to handle these failures in a controlled and predictable way.
By default, a cloud flow step only runs if the previous step is successful. If your external service fails or times out, all subsequent steps will normally skip, causing the flow to fail silently from the perspective of the Copilot Studio agent.
How to Configure the Flow for Failure Handling
To meet your requirements, you need to create a parallel or sequential failure path using the following steps:
1. Add your Notification Action
2. Configure "Run After" Settings
Incorrect:
* Retry the failing action without handling failure.
* Send a notification with relevant error details.
Reference:
https://www.matthewdevaney.com/3-power-automate-error-handling-patterns-you-must-know/
23. Frage
Case Study 1 - Blue Yonder Airlines
Background
Blue Yonder Airlines is a global carrier headquartered in Los Angeles, California, operating domestic and international flights. The company serves millions of passengers annually through its website, mobile app, and call centers. To improve customer service efficiency and reduce call center volume, Blue Yonder is deploying an AI agent in Microsoft Copilot Studio.
The agent will handle customer inquiries across multiple channels - web chat, mobile app, and Microsoft Teams (for internal support staff). It will answer questions, retrieve data from enterprise systems, and escalate to human agents when needed.
The project is led by a cross-function team:
- Product manager: Defines requirements and success metrics.
- Lead agent author: Designs topics, intents, and generative behavior.
- Flow designers: Build agent flows and integrations.
- IT/security and compliance: Oversees identity, data protection, and Responsible AI (RAI) compliance.
Current environment
Channels
Public website: Embedded web chat
Mobile app: In-app chatbot
Microsoft Teams: Internal support agent access
Identity and access
Customers: Anonymous access for general inquiries (e.g., flight status, baggage policy).
Authentication is required for personal data access (e.g., bookings, loyalty points).
Internal staff: Authenticate via Microsoft Entra ID.
Data sources
Reservation and Ticketing System (internal): REST API, no prebuilt connector with custom enterprise database.
Flight Status and Weather APIs (external): REST APIs with API keys.
Customer Support Knowledge Base: SharePoint library with PDFs and policy documents.
Loyalty Program Data: Stored in Dynamics 365 and Dataverse.
Travel Advisory Content: Uses REST API with partner services.
Integration mechanisms
Custom connectors must be used for internal APIs that lack prebuilt connectors.
HTTP request nodes may be used for lightweight external APIs.
Knowledge sources must be used for unstructured content.
Agent flows must be used to encapsulate reusable logic (e.g., rebooking).
Business requirements
Omnichannel support
Deploy the agent across web, mobile, and Teams with a consistent user experience. The Teams deployment must also support internal staff.
Self-service capabilities
The agent must handle common inquiries such as:
- Flight status
- Booking and rebooking
- Loyalty program questions
- Travel policies and baggage rules
Human escalation
If the agent cannot resolve an issue or the user requests help, it must:
- Escalate to a human agent.
- Transfer the conversation transcript and relevant context.
- Redact any sensitive personal data before escalation.
Knowledge integration
The agent must use scalable methods for knowledge integration and must not rely on manually authored Q&A topics for each document.
Performance metrics
First-contact resolution: +25%
Tier-1 call deflection: ≥20%
Response time: 90% of queries answered within 30 seconds
Accuracy: ≥95% for known FAQs
CSAT: ≥85% for AI-handled interactions
Technical requirements
Platform constraints
No custom code is permitted; only Copilot Studio's built-in tools may be used.
All backend logic must be implemented using agent flows.
Markdown must be used for formatting (e.g., bold, bullet points); HTML is not supported.
Authentication
Sign-in is required for personal data access.
Anonymous access is allowed for general inquiries.
User identity must be used for data access; shared or builder credentials must not be used.
Compliance and security
Power Platform DLP policies must be enforced to block unauthorized data flows.
Responsible AI content moderation filters must be enabled.
Prompt modifications must be added to enforce tone, disclaimers, and refusal behavior.
Disclaimers must be applied consistently across all generative responses. Manual edits to individual topics must be avoided.
Monitoring and maintenance
All conversations and actions must be logged for auditing.
Weekly reviews of transcripts and metrics must be conducted.
Topics, flows, and knowledge sources must be updated as policies or systems evolve.
Issues and constraints
API rate limits: External APIs (e.g., flight status) have usage limits. Agent flows must handle retries and caching to avoid exceeding quotas.
Knowledge base limits: Copilot Studio has limits on the number and size of indexed documents.
Large files must be split or summarized.
Generative answer risks: Generative responses must be constrained to avoid policy violations.
Prompt modifications and filters must be used to enforce tone, safety, and compliance.
User input variability: Users phrase questions in diverse ways. Topics must include varied trigger phrases and fallback handling.
Authentication UX: The agent must clearly explain when sign-in is required and handle transitions smoothly across channels.
Problem statement
Blue Yonder Airlines must deploy a secure, scalable, and policy-compliant AI agent using Microsoft Copilot Studio. The agent must deliver accurate, helpful, and safe responses across multiple channels, integrate with enterprise systems, and support both anonymous and authenticated users. It must adhere to strict data protection and Responsible AI standards while improving customer service efficiency and satisfaction.
You need to deploy the Blue Yonder Copilot agent to the public website and Microsoft Teams while ensuring compliance with the company's security and Responsible AI requirements.
Which two actions should you perform before making the agent available on both channels? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Antwort: A,C
Begründung:
Under Compliance and Security, the requirements state: "Prompt modifications must be added to enforce tone, disclaimers, and refusal behavior. Disclaimers must be applied consistently across all generative responses. Manual edits to individual topics must be avoided." Setting this up at the system level ensures broad, consistent compliance without violating the constraint against manual topic-level edits.
Under Compliance and Security, the case study explicitly states: "Power Platform DLP policies must be enforced to block unauthorized data flows." DLP (Data Loss Prevention) policies are environment-level safeguards configured before deployment to restrict data movement between custom/REST connectors and non-compliant channels/destinations.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/publication-fundamentals-publish-channels
https://learn.microsoft.com/en-us/microsoft-copilot-studio/advanced-dlp
https://learn.microsoft.com/en-us/microsoft-copilot-studio/nlu-generative-answers
24. Frage
Users can add content to a paragraph field in a team-managed project, but they cannot @mention other users.
Identify the cause.
Antwort: F
Begründung:
Comprehensive and Detailed Explanation From [Atlassian Jira Cloud Documentation] The cause is the Jira Browse users and groups global permission. Atlassian states that users with this permission can view user names when working with user-picker fields or @mentioning users. Without it, Jira cannot provide the affected users with the complete user directory required to search for and select another person through an @mention.
The fact that users can enter and save ordinary text in the paragraph field confirms that the field is present, editable, and available on the applicable work-item layout. Therefore, field settings and screen settings are not responsible. Notification settings determine how users receive updates after an event and do not control whether names appear in the mention picker.
Project permissions and issue-level security can affect whether a particular person can access the project or work item. However, a general inability to search for or mention other users indicates that the initiating users lack the site-wide user-browsing capability.
A Jira administrator should review Settings → System → Global permissions and grant Browse users and groups to an appropriate group containing the affected users, subject to the organization's privacy policies.
Relevant documentation topic: Jira Cloud administration → Global permissions → Browse users and groups. See What are global permissions, and what do they control?.
25. Frage
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