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| Section | Objectives |
|---|---|
| Topic 1: Build and Configure AI Agents in Copilot Studio | - Configuring actions, plugins, and connectors - Creating and configuring copilots and topics - Using generative AI and prompt engineering in Copilot Studio |
| Topic 2: Deploy, Monitor, and Optimize AI Agents | - Monitoring performance and analytics - Iterative improvement and lifecycle management - Publishing and deploying copilots |
| Topic 3: Integrate External Systems and Data Sources | - Connecting Azure services and APIs - Using Power Automate for workflow orchestration - Integrating Dataverse and Power Platform components |
| Topic 4: Governance, Security, and Compliance | - Managing access control and authentication - Data protection and responsible AI practices - Environment management in Power Platform |
| Topic 5: 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 |
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NEW QUESTION # 100
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 configure the Blue Yonder Copilot agent's responses in accordance with the company's content control and platform requirements.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,C
Explanation:
Scenario: 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.
[C] Markdown must be used for formatting (e.g., bold, bullet points); [Not D] HTML is not supported.
[B] Disclaimer text.
The best approach is to configure prompt instructions that include disclaimer text.
This method ensures the disclaimer is applied universally to all AI-generated responses without manual duplication across topics. It scales effectively and reduces maintenance overhead.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/authoring-instructions
NEW QUESTION # 101
A team has an existing agent in Copilot Studio and must move it to a different target location without rebuilding the agent. Some builders use Copilot Studio in the web app, while others use Copilot Studio in Microsoft Teams.
You need a supported approach to move the existing agent to the target location by using ALM practices. The solution must avoid rebuilding the agent.
What should you do?
Answer: C
Explanation:
Microsoft Copilot Studio uses Power Platform solutions as the supported packaging and transport mechanism for application lifecycle management. Because the agent already exists, the correct process is to create a custom solution in the source environment and add the existing agent to that solution. The solution captures the agent and its dependent components so they can be exported as a package and imported into the target environment. This avoids rebuilding topics, variables, tools, and other agent configuration manually.
Power Platform pipelines can automate deployment after the agent has been packaged, but a pipeline does not independently discover and package an agent that has not been added to a solution. Consequently, option A omits a required dependency. Manually duplicating the agent, as proposed in option C, is not a controlled ALM process and introduces configuration drift between environments.
Option D is also incorrect. Agent topics and related components should not be removed directly from a solution to prepare an export. Microsoft warns that deleting or manipulating dependent agent components independently can create missing dependencies and cause export or import failures.
After import, environment-specific items-such as authentication, connections, environment-variable values, and channels-may require verification before the agent is published in the target environment. Relevant study-guide areas are Manage solutions , Export and import agents , and Implement ALM . See Export and import agents using solutions .
NEW QUESTION # 102
A company needs to ground an agent's answers in Copilot Studio by using live enterprise data from a supported system through a Microsoft Power Platform connector.
To meet the business requirements, the builder must:
* Authenticate by using a valid connection.
* Add the connector as a real-time knowledge source.
* Select the tables used for knowledge grounding.
You need to add a Power Platform connector as a real-time knowledge source for the agent.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
The process begins from the agent's Overview or Knowledge page, or from the properties of a generative answers node, by selecting Add knowledge . This opens the catalog of supported knowledge-source types.
The builder must then select the required Power Platform connector from the available real-time connectors, using the advanced selections if the connector is not displayed in the featured list.
Authentication must occur before table selection because Copilot Studio needs an authorized connection to access the target system and discover its available schema. The builder signs in and either creates a new connection or reuses a valid existing connection. After selecting the target location and completing authentication, Copilot Studio presents the tables or entities available through that connection.
The builder then selects only the tables required for knowledge grounding. Limiting the selection improves relevance and reduces unnecessary exposure of enterprise data. After optionally supplying the knowledge- source name, description, synonyms, or glossary definitions, the builder selects Add to agent to complete the configuration.
This is a real-time knowledge integration: connector requests retrieve current data at runtime instead of treating the connector as a static uploaded document. Relevant study-guide topics are Connect to Microsoft Power Platform connectors , Configure advanced responses with custom knowledge sources , and Connect to enterprise knowledge sources . See Add Power Platform connectors as real-time knowledge .
NEW QUESTION # 103
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.
Answer: B,C
Explanation:
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
NEW QUESTION # 104
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.
Answer:
Explanation:
NEW QUESTION # 105
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