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| Section | Weight | Objectives |
|---|---|---|
| Test, deploy, and manage agents | 20–25% | - Deploy and manage agent lifecycle
|
| Build and extend agents in Copilot Studio | 40–45% | - Develop agent flows and logic
|
| Plan and configure agent solutions | 30–35% | - Plan an agent solution
|
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NEW QUESTION # 23
You run the same fixed test set three times in Copilot Studio.
During evaluation, you observe the following:
The same interaction fails in all three runs.
Score values range from 0.58 to 0.61.
The reasoning states that the response partially matches the expected answer.
The knowledge source that is used is internal documentation.
No tools are invoked.
Answer:
Explanation:
Explanation:
Repeated failures # Evidence of a recurring pattern; Response-quality information # The response partially matches the expected answer; Evaluation limitation # The specific underlying cause cannot be determined from the score alone.
Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: Three failures on the same fixed interaction, with scores tightly clustered between 0.58 and 0.61, provide evidence of a repeatable problem rather than an isolated stochastic miss. The evaluator ' s reasoning directly supports the conclusion that the response only partially represents the expected answer. The run also shows that internal documentation was retrieved and no tools were involved, which narrows the diagnostic path toward the knowledge, instructions, retrieval quality, or expected-answer design. It does not, however, prove a single root cause. A score and short reasoning statement cannot tell whether the source document is incomplete, the wrong passage ranked highly, the question is ambiguous, the model omitted a key condition, or the expected response is too strict. The next step is to inspect the test transcript, retrieved sources, activity map, and expected answer, then change one factor and rerun the same case. This disciplined triage distinguishes what the evaluation demonstrates from what still requires investigation and prevents an unsupported causal conclusion. Study Guide alignment: Test and manage agents > Evaluate agent performance > Review test results.
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 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.
Answer:
Explanation:
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
NEW QUESTION # 25
An agent in Copilot Studio already exists in a development environment.
You need to prepare the agent so it can be moved to a production environment using the supported solution-based approach.
You need to create an exportable package that contains the existing agent.
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:
Agents authored in Copilot Studio land in the Common Data Service Default Solution, which can't be exported. The supported ALM path is to create your own (unmanaged) solution in the dev environment and add the existing agent to it, along with its dependencies (topics, custom connectors, flows, environment variables, connection references).
Exporting produces the deployable package (.zip). Dev environments hold the unmanaged source solution, and the export step is where you generate the package - choosing managed output at export time when the target is production.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/authoring-solutions-import-export
NEW QUESTION # 26
An agent in Copilot Studio must retrieve real-time data from an external system that exposes a REST API. The API returns structured JSON responses and requires authentication.
To meet the business needs, the solution must meet the following requirements:
The agent calls a REST API during a conversation.
The REST API call runs with proper authentication.
The REST API response is available to the topic at runtime.
You need to configure the REST API to meet the requirements.
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:
NEW QUESTION # 27
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 using ALM practices. The solution must avoid rebuilding the agent.
You need to move the existing agent to the target location using a supported method.
What should you do?
Answer: B
Explanation:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/authoring-solutions-import-export
NEW QUESTION # 28
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