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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Test and Manage Agents | 20-25% | - Monitor and manage agent solutions
|
| Topic 2: Plan and Configure Agent Solutions | 30-35% | - Plan agent solutions
|
| Topic 3: Integrate and Extend Agents in Copilot Studio | 40-45% | - Integrate agents with enterprise systems
|
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NEW QUESTION # 74
A company wants an agent to retrieve real-time answers from a large, frequently updated document repository stored outside Dataverse, with strong relevance ranking. Which knowledge source should you configure?
Answer: A
Explanation:
Azure AI Search provides indexed, relevance-ranked retrieval over large and frequently updated content, making it the preferred grounding source for enterprise-scale RAG scenarios compared to static file uploads.
NEW QUESTION # 75
A team uses environment variables to separate configuration from an agent so that the same solution can be moved between environments.
The team requires the following scenarios to be addressed:
An administrator updates a sensitive configuration value and needs the change to take effect at runtime without republishing the agent.
A builder needs to confirm a configuration value but must not be able to modify it from Copilot Studio.
An administrator updates a non-secret configuration value and needs the updated value to be used by the published agent at runtime.
Answer:
Explanation:
Explanation:
Sensitive value without republishing # Use a secret environment-variable type for runtime resolution; View but do not edit in Copilot Studio # Environment variables are read-only in Copilot Studio; Updated non-secret value used by the published agent # Republish the agent.
Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: Secret environment variables are designed to resolve protected values, typically through Azure Key Vault, without embedding the secret in the agent definition. That makes them suitable when an administrator changes a sensitive value and runtime must obtain the current secret. Environment variables exposed to Copilot Studio are read-only there; administrators manage their definitions and current values through the Power Platform solution and environment controls, so a builder can inspect a reference without editing it on the topic canvas. For ordinary non-secret variables used by a published agent, a configuration change must be followed by republishing so the active runtime version consumes the updated value. Topic variables are conversation state and are not an ALM substitute for environment-specific configuration. Allowing every builder to edit environment variables from Copilot Studio would defeat separation of duties. The team should test the exact behavior for each variable type, because secret resolution and published configuration have different lifecycles, and should ensure target-environment service identities can read the referenced Key Vault secret. Study Guide alignment:
Test and manage agents > Implement application lifecycle management for agents in Copilot Studio > Create and use environment variables.
NEW QUESTION # 76
A team is preparing to evaluate an agent in Copilot Studio before expanding access to additional users.
The team must select an evaluation method that meets the following requirements:
- Use a fixed set of prepared interactions.
- Determine responses against a predefined baseline.
- Support consistent comparison across repeated test runs.
You need to choose an evaluation method that meets the evaluation requirements.
What should you do?
Answer: B
Explanation:
You should apply a standardized evaluation matrix. This method uses reusable test sets and automated scoring to match your fixed interactions and baseline requirements before live user rollout.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/analytics-agent-evaluation-overview
NEW QUESTION # 77
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 ensure that every AI-generated response from the agent in Copilot Studio includes a disclaimer that complies with the company's security and governance policies.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
[B]
Using prompt modification in the Generative Answers node settings is an excellent and highly effective strategy for embedding compliance disclaimers into your agent's responses.
[A]
Adding a mandatory disclaimer to your greeting message is the best practice for agent governance. You can enforce this by configuring your Greeting System Topic and adding formal
"Output Rules" to the agent's core instructions.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/nlu-boost-node
https://www.reddit.com/r/copilotstudio/comments/1pje5fy/how_do_you_handle_ai_disclaimers_in
_copilot/
NEW QUESTION # 78
Drag and Drop Question
A solution containing an agent in Copilot Studio is deployed to multiple environments that require different configuration values.
Configuration values must vary by environment without modifying the solution contents.
Configuration values must meet the following requirements:
- Allow values to be set during import.
- Exclude values from the agent definition.
- Run a dedicated agent instance for each individual environment.
You need to associate each requirement with the configuration approach.
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:
Explanation:
Box 1: Define environment variables in the solution.
The correct ALM-related configuration to use for this requirement is Environment Variables.
You must use Environment Variables (specifically with the Current Value left blank in the development solution) to allow different configuration values across multiple environments without modifying the solution contents. When you export the solution as managed and import it into a target environment, the system will explicitly prompt the administrator to set the values during import.
Box 2: Define environment variables in the solution
Define environment variables in the solution is the correct Application Lifecycle Management (ALM) configuration to use for this requirement.
Separates Data from Code: Environment variables act as parameters that store configuration keys and values separately from your agent's definition.
Preserves Solution Integrity: You can transport the same solution across development, testing, and production environments without modifying any of its internal contents.
Excludes Hardcoded Values: They explicitly remove the need to embed sensitive or environment- specific data directly inside the agent, satisfying your exact requirement.
Box 3: Define environment variables in the solution
You should use Environment Variables combined with Connection References within Power Platform / Copilot Studio.
Define parameters centrally: Create environment variables inside your solution to act as placeholders for your configuration values (like URLs, IDs, or API keys).
Keep the solution generic: Your Copilot agent references these variables rather than hardcoded values.
Set values per deployment: When you import the managed solution into a new environment (Test, Production, etc.), the deployment pipeline prompts you to provide the specific values for that environment.
Isolate agent traffic: Because the variables point to environment-specific backend services, data sources, or IDs, a single solution definition naturally spins up and drives a dedicated, isolated agent instance in each environment.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/guidance/alm
NEW QUESTION # 79
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