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Microsoft AB-620 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Deploy, Monitor, and Optimize AI Agents | - Monitoring performance and analytics - Publishing and deploying copilots - Iterative improvement and lifecycle management |
| Build and Configure AI Agents in Copilot Studio | - Using generative AI and prompt engineering in Copilot Studio - Configuring actions, plugins, and connectors - Creating and configuring copilots and topics |
| Governance, Security, and Compliance | - Environment management in Power Platform - Managing access control and authentication - Data protection and responsible AI practices |
| 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 |
| Integrate External Systems and Data Sources | - Connecting Azure services and APIs - Using Power Automate for workflow orchestration - Integrating Dataverse and Power Platform components |
Microsoft Designing and Building Integrated AI Agent Solutions in Copilot Studio Sample Questions:
Hotspot Question
A company uses an agent that invokes an agent flow to exchange information during a conversation.
The company requires that the agent send data into the flow and receive structured results back from the same flow run. To support this business need, the flow must be configured to do the following:
- Capture the data provided by the agent.
- Return data results to the agent.
You need to configure the flow so that it can exchange data with the agent.
What should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Box 1: Add a text input parameter
To allow a Copilot Studio agent to pass data into a flow and receive structured results back, you must configure a Power Automate flow triggered by the "Run a flow from Copilot" trigger with custom input variables, and concluding with the "Respond to Copilot" action containing structured output parameters.
Box 2: Add a text output parameter
To return structured results from an agent flow to your agent in Microsoft Copilot Studio, you should configure the "Respond to the agent" action in your flow with an output parameter.
In Microsoft Copilot Studio, when a topic or bot calls a Power Automate flow, data is sent into the flow via input parameters. To pass data, objects, or structured results back to the copilot during the exact same flow run, you must explicitly define one or more output parameters (such as a text, number, or boolean output) in the "Return value(s) to Power Virtual Agents/Copilot Studio" final step of the flow.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/advanced-use-flow
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.
Correct Answer:
Explanation:
Scenario: Problem statement: The agent must adhere to strict data protection and Responsible AI standards while improving customer service efficiency and satisfaction.
Step 1: Define Data Loss Prevention (DLP) policies
Security and compliance must be established at the foundation level. Admins use the Power Platform admin center to configure DLP policies. This establishes environmental guardrails by restricting unauthorized data connectors or preventing the agent from leaking sensitive data.
Step 2: Add custom prompt modifications
Once the environment is secure, the maker configures the specific behavior of the agent. Custom prompt modifications and agent instructions shape the tone, define conversational topics, and establish explicit boundaries for the generative model (e.g., instructing the model to strictly stay within its knowledge base).
Step 3: Conduct adversarial testing
This is the final evaluation phase before deployment. After the guardrails and custom prompts are in place, you simulate real-world attacks (such as jailbreaks, prompt injections, or malicious user inputs) to verify that the agent reliably adheres to your corporate guidelines and Microsoft's Responsible AI standards.
Reference:
https://learn.microsoft.com/en-us/troubleshoot/power-platform/copilot-studio/generative-answers/agent-response-filtered-by-responsible-ai
An agent needs to call an external REST API that isn't available as a pre-built connector, and the API requires OAuth 2.0 authentication. What should you create?
- A. A generative answers node
- B. A new environment variable only
- C. A custom connector with an OAuth 2.0 authentication type
- D. A Power Automate flow with anonymous authentication
Correct Answer: C 🗳️
Explanation: Only visible for Prep4sureExam members. You can sign-up / login (it's free).
Hotspot Question
You are setting up an evaluation for an agent in Copilot Studio before expanding access to more users.
You must configure the evaluation so that the agent is tested by using prepared interactions rather than impromptu conversations.
The evaluation should provide the following functionality for users:
- An administrator updates a sensitive configuration value. The
administrator requires 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
requires the updated value to be used by the published agent at
runtime.
You need to create a test set-based evaluation that supports consistent test runs for each scenario.
What should you configure for each scenario? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Box 1: Secret environment variable
The correct configuration for updating a sensitive configuration value at runtime without having to republish the agent is a Secret environment variable.
Secret environment variables: Securely store secrets (like passwords, keys, or API tokens) within the environment and reference them directly inside the agent. These can be updated dynamically at runtime without needing to republish the bot, as they are evaluated in real-time.
Incorrect:
Only in production / Only after publishing: These configurations require a full publishing cycle to take effect. This means that any adjustments to bot topics, node logic, or regular environment variables require pushing the latest content updates out to end-users.
Box 2: Managed environment variable
A managed environment variable is the specific configuration needed. It enables makers to reference or confirm configuration values without permitting modifications directly within the studio interface, protecting agent integrity. Additionally, you can configure your testing using the Agent Evaluation in Copilot Studio feature.
Box 3: String environment variable
The specific configuration that allows an administrator to update a non-secret configuration value and requires the updated value to be used by the published agent at runtime is a string environment variable.
In Microsoft Copilot Studio, environment variables are used to manage configuration values dynamically. When an administrator updates a non-secret string environment variable, the change takes effect at runtime without requiring the agent to be rebuilt or modified in the authoring canvas.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/analytics-agent-evaluation-overview
Drag and Drop Question
A company is preparing an agent flow so that it can be invoked by an agent during conversations.
The agent flow must meet the following requirements:
- The agent must be able to trigger the flow.
- The agent flow must be verified.
You need to prepare an agent flow so that it can be used by the agent.
In which order should you perform the actions to prepare the agent flow? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Correct Answer:
Explanation:
Step 1: Create a new agent flow artifact in the environment
You must first initialize the flow container within your Copilot Studio workspace before adding any logic.
Step 2: Configure the invocation trigger that allows the agent to call the agent flow You must set up the specialized trigger first (i.e., "When an agent calls the flow"), because this defines the required input variables that the downstream behavioral actions will rely on.
Step 3: Configure the actions that implement the required behavior
With the trigger and input variables established, you build the functional core of the flow (e.g., pulling data, sending approvals) and finalize it with the "Respond to the agent" action.
Step 4: Validate the agent flow behavior with representative test runs
The workflow needs to be tested and verified directly within the flow authoring interface using test data to check for errors before exposing it to the orchestrator.
Step 5: Make the agent flow available for agent usage
Once verified, you publish the flow and add it to your agent as an active tool so the conversational orchestrator can dynamically call it during live interactions.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/flow-agent
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