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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Test, deploy, and manage agents | 20–25% | - Test and validate agent performance
|
| Topic 2: Plan and configure agent solutions | 30–35% | - Configure agent foundations
|
| Topic 3: Build and extend agents in Copilot Studio | 40–45% | - Implement advanced capabilities
|
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NEW QUESTION # 23
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: A,B
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 # 24
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.
Drag and Drop Question
You need to determine how the agent in Copilot Studio should integrate with two different systems in the Blue Yonder environment, based on their characteristics and the tools available.
Which integration mechanism should you use for each system? To answer, move the appropriate integrations to the correct system or data sources. You may use each integration 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: Custom Connector
Internal Reservation
A Custom Connector is preferable to a HTTP request in this enterprise environment.
Scenario:
Data sources
Reservation and Ticketing System (internal): REST API, no prebuilt connector with custom enterprise database.
Custom connectors must be used for internal APIs that lack prebuilt connectors.
Box 2: HTTP request
Flight Status and Weather Service
Scenario:
Flight Status and Weather APIs (external): REST APIs with API keys
HTTP request nodes may be used for lightweight external APIs.
Reference:
https://learn.microsoft.com/en-us/connectors/custom-connectors/
NEW QUESTION # 25
You need to implement tool usage in topics that meet the Blue Yonder design and governance requirements.
Which implementation method should you use for each requirement? To answer, move the appropriate implementation methods to the correct requirements. You may use each implementation method once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Requirement
Implementation method
Let the agent decide when to run a tool based on user input.
Enable generative orchestration.
Run a specific agent flow at a defined step in a topic.
Add a tool node.
Use a connector action to retrieve data during a conversation.
Add a tool node.
Generative orchestration is appropriate when the agent must independently determine whether a tool is relevant to the user's request. Copilot Studio evaluates the user's message, conversation context, and the tool' s name, description, inputs, and outputs before deciding whether to invoke it. Microsoft documentation confirms that generative orchestration allows an agent to choose tools based on their descriptions and the current user query.
When an operation must occur at a predetermined point in an authored topic, the operation should instead be inserted explicitly by using an Add a tool node. Selecting an agent flow from that node ensures that the flow executes at that exact position in the topic rather than being selected autonomously by the orchestrator.
The same node type is used when calling a connector action. From the topic canvas, the author selects Add a tool , chooses Connector , and then selects and configures the required connector operation. Adaptive Cards present interactive information, while Question nodes collect user input; neither directly invokes flows or connectors.
Relevant study-guide area: Plan and configure agent solutions # Configure topics # Add agent flows to a topic and Add tools to a topic . See Orchestrate agent behavior with generative AI , Add tools to custom agents , and Use connectors in Copilot Studio agents .
NEW QUESTION # 26
Drag and Drop Question
A company has an Azure AI Search vector index that contains enterprise content and wants to use it as a knowledge source for an agent in Copilot Studio.
To meet business needs, the knowledge source must meet the following requirements:
- The agent must connect to the Azure AI Search service.
- The agent must use a supported authentication method for the
connection.
- The agent must ensure answers are grounded using the specified Azure
AI Search index.
You need to connect to Azure AI Search as a 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:
Step 1: Initiate the process to add a knowledge source to the agent
You start inside Copilot Studio by navigating to your agent's settings and choosing to add a new knowledge source.
Step 2: Create a new Azure AI Search connection
From the available knowledge source options, you select Azure AI Search, which prompts the creation of a new connection.
Step 3: Configure the authentication method and provide the required connection details You input the necessary credentials (such as your Azure AI Search endpoint and API key or OAuth details) to authenticate the connection securely.
Step 4: Specify the Azure AI Search index to use for grounding
Once connected, you select the specific vector index that contains your enterprise content so the model knows where to look.
Step 5: Associate the configured knowledge source with the agent
Finally, you save and link this configured search source to the agent, enabling it to ground its responses using your enterprise data.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/knowledge-azure-ai-search
NEW QUESTION # 27
You are designing an agent that must reason over multiple tools and decide autonomously which tool to invoke based on user intent, rather than following a fixed topic sequence. Which Copilot Studio capability should you use?
Answer: B
Explanation:
Generative orchestration lets the agent dynamically select and chain topics, tools, and knowledge sources at runtime based on reasoning over the user's request, instead of relying on rigid, pre- authored conversation paths.
NEW QUESTION # 28
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