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| Section | Objectives |
|---|---|
| Deploy, Monitor, and Optimize AI Agents | - Iterative improvement and lifecycle management - Monitoring performance and analytics - Publishing and deploying copilots |
| Build and Configure AI Agents in Copilot Studio | - Configuring actions, plugins, and connectors - Using generative AI and prompt engineering in Copilot Studio - Creating and configuring copilots and topics |
| Governance, Security, and Compliance | - Managing access control and authentication - Environment management in Power Platform - Data protection and responsible AI practices |
| Integrate External Systems and Data Sources | - Integrating Dataverse and Power Platform components - Using Power Automate for workflow orchestration - Connecting Azure services and APIs |
| 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 |
1. Drag and Drop Question
A company is building an agent in Copilot Studio topic that must retrieve real-time status information from an external REST API using a Send HTTP request node.
To meet the business needs, the topic must meet the following requirements:
- The request must include the required authentication header.
- The request must call the endpoint using the correct HTTP method.
- The response must be configured with an appropriate response data
type based on a schema and saved so the topic can reuse the returned
values.
You need to configure the Send HTTP request node.
Which configuration 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.
2. 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.
3. Drag and Drop Question
A team wants to automate solution deployment between environments. Deployments must follow a controlled, repeatable process.
The team needs to implement a pipeline-based deployment process that enforces the correct dependency order.
You need to implement a deployment pipeline and determine the correct sequence of actions.
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.
4. Drag and Drop Question
A company plans to use Copilot connectors to ground answers from a third-party system for an agent in Copilot Studio.
To meet the business needs, responsibilities for configuring and using Copilot connectors must be separated, based on the following requirements:
- For tenant administration, the connector must be available at the
tenant level.
- For agent runtime behavior, answers must be grounded from authorized
sources.
- For builder configuration, you must add the connector to the agent.
You need to determine how Copilot connector set-up responsibilities are handled across tenant, runtime, and builder stages.
What should you configure for each requirement? To answer, move the appropriate configuration 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.
5. 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 configure the agent in Copilot Studio to meet Blue Vender's Responsible AI and data protection requirements.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
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