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Microsoft Designing and Building Integrated AI Agent Solutions in Copilot Studio 認定 AB-620 試験問題 (Q121-Q126):

質問 # 121
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.

• A. Add required disclaimer text inside each individual topic.
• B. Duplicate disclaimer text across reusable topics.
• C. Configure prompt instructions that include disclaimer text.
• D. Use Markdown syntax within response content.
• E. Insert HTML formatting directly into topic responses.

正解：C、D

解説：
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

質問 # 122
An agent calls a flow to a database. The agent requires structured output values to be returned. The agent receives unexpected or empty values.
You need to configure the agent so that data is exchanged correctly between the agent and the flow. What should you do?

• A. Republish the agent.
• B. Validate parameter definitions.
• C. Change the trigger schema.
• D. Add a Parse JSON inside the flow.
• E. Review the flow run history.

正解：B

解説：
Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: The first configuration check is the contract between the agent and the flow. Input and output parameter definitions must agree on name, data type, required status, and mapping. A flow can complete successfully yet return an empty value to the agent if the response field is not mapped to the declared output or if the agent expects a different type.
Validating the parameter definitions therefore addresses the actual data-exchange requirement. Reviewing run history is useful for diagnosis, but it does not correct a mismatched contract by itself. Changing the trigger schema or adding Parse JSON might be necessary in a specific implementation, yet neither should be done before confirming what the caller sends and what the flow returns. Republishing cannot repair an incorrect mapping. The builder should test the flow directly with known values, inspect each action output, verify the final response action, and then test from the agent. Optional and null fields should be handled explicitly, and sensitive outputs should be minimized and protected in logs. Study Guide alignment: Plan and configure agent solutions > Create and monitor agent flows in Copilot Studio > Add input and output parameters.

質問 # 123
Case Study 2 - Fabrikam Inc.
Background
Fabrikam Inc. is a Canada-based manufacturer with a growing service organization that supports field technicians and internal operations teams. Fabrikam Inc. plans to launch a new internal agent solution named Operations Concierge to reduce time spent searching policy content, retrieving operational metrics, and executing routine transactions.
The agent will be used by three groups:
- Service coordinators who triage incoming service requests
- Field technicians who need guided procedures and parts availability
- Operations managers who monitor KPIs and exceptions
The agent solution must work in real-world operational conditions. Users often ask questions mid- call with a customer or while coordinating parts shipments. The agents require quick, reliable outcomes. As a result, Fabrikam Inc. requires the solution to:
- Provide grounded answers with traceability when it provides guidance.
- Retrieve real-time metrics when users ask for operational status.
- Execute authenticated updates when users initiate a flow (such as creating a parts request).
Fabrikam Inc. also expects the solution to be maintained by multiple makers and developers across the year. The company has experienced duplicated logic and inconsistent behavior across different agents. This project emphasizes reuse, governance, and maintainability across teams.
Current environment
Fabrikam Inc. runs three Microsoft Power Platform environments for agent development and release: Dev, Test, and Prod.
The team plans to build the agent and validate it in Dev and Test, then promote to Prod by using a controlled release process that supports repeatable deployments.
Fabrikam Inc. already has two assets the team wants to reuse:
- A partially completed Copilot Studio agent named Service Desk Agent, used by IT to create internal tickets and route requests
- A Microsoft Foundry agent created by a central AI team that performs specialized summarization and classification for long-form text (for example, summarizing call transcripts into an incident narrative) Fabrikam Inc. also has operational and knowledge data sources:
- A curated policy library (internal SOPs, service warranty rules, escalation criteria, and standard operating procedures)
- A set of indexed documents and procedures in an Azure AI Search service that supports vector search for the policy library
- A Microsoft Fabric workspace that includes a semantic model used by operations leadership for reporting Business requirements Fabrikam Inc. requires Operations Concierge to meet the following business requirements:
- Traceability requirement: When the agent provides policy guidance or procedural recommendations, users must be able to see where the answer came from.
- Metrics requirement: When users ask about service performance (backlog, SLA risk, parts shortages, dispatch delays), the solution must return up-to-date metrics in a structured format that operations managers can use in weekly reviews.
- Transaction requirement: The solution must support authenticated updates initiated during conversations, including creating a parts request and updating a service case status.
In addition, Fabrikam Inc. wants to avoid duplicating common assets across agents:
- The team must reuse the same set of escalation topics, MCP tool definitions, and a standard safety disclaimer across three different agents.
- Only the platform engineering group as allowed to edit shared assets. However, all agent authors must be able to use them.
Technical requirements
The Fabrikam Inc. solution architecture uses a multi-agent approach so that specialist responsibilities are isolated and can evolve independently.
The Operations Concierge (primary agent) must coordinate the following specialist capabilities:
- Policy and procedure Q&A: Use an enterprise knowledge source that supports indexed retrieval across the curated policy library and service procedures.
- Operational metrics: Delegate metric queries to a Fabric Data Agent that reads governed business data through the Fabric semantic model.
- Authenticated updates: Use tools exposed by an existing internal Model Context Protocol (MCP) server that provides transactional operations for the service organization.
- Specialized processing: Delegate summarization and classification requests to an existing Microsoft Foundry agent.
Fabrikam Inc. will onboard two MCP servers as tools:
- PartsOps MCP server: exposes tools for parts availability checks and parts request creation.
The server requires per-user authentication because actions must be traceable to the requesting user.
- WarrantyRules MCP server: exposes a read-only tool for validating warranty coverage. The server uses an API key shared by the agent team.
Fabrikam Inc. has also defined a collaboration requirement with the existing Service Desk Agent:
- The primary agent must delegate IT-specific requests to the existing Service Desk Agent rather than reimplement ticket creation logic.
Finally, Fabrikarn Inc. plans to support a partner integration:
- For shipment tracking inquiries, Fabrikam Inc. will delegate to a partner-provided agent that is only available through a standardized agent-to-agent endpoint.
Issues and constraints
During early testing, Fabrikam Inc. found three recurring problems:
- Makers are copying and modifying the same components across agents, resulting in inconsistent disclaimers and duplicated tools.
- Users can obtain a correct answer, but the response is not consistently traceable to a source when the agent uses knowledge.
- The primary agent can route some requests, but specialist capabilities are not consistently delegated (for example, some metric questions are answered generatively instead of being routed to the Fabric Data Agent).
You are part of the engineering team responsible for correcting the design and configuration to meet the preceding requirements and constraints.
Drag and Drop Question
You need to connect Operations Concierge to Fabrikam Inc.'s Azure AI Search knowledge index while complying with security requirements.
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.

正解：

解説：

Explanation:
Box 1: Select the service principal as the authentication type
When Copilot Studio connects to Azure AI Search, you choose an authentication type for the connection. Copilot Studio supports adding Azure AI Search as a knowledge source, and by default this is set up using key-based authentication. However, keys are shared secrets with no identity tied to them - they don't satisfy governance requirements around authenticated, auditable access. A service principal (Microsoft Entra ID app registration) is different: a service principal is an identity that represents an application and allows it to access resources in your tenant, and for security and compliance reasons, Copilot Studio uses federated identity. Using a service principal means the connection authenticates as a distinct Entra ID identity with role- based permissions (e.g., "Search Index Data Reader") rather than a static key - which is what
"governance requirement for authenticated access" is pointing at.
Box 2: Enter the name of the Azure AI Search index
An Azure AI Search service can host multiple indexes. Simply pointing at the service isn't enough
- Copilot Studio supports vectorized indexes using integrated vectorization, and when setting up the knowledge source you must select/name the specific index that contains your curated content (e.g., the "policy and procedure Q&A" index) rather than some other index that might live on the same service.
Box 3: Provide the Azure Search Endpoint URL in the connection details
The Endpoint URL (e.g., https://<search-service-name>.search.windows.net) identifies which Azure AI Search service instance the connection points to - this is how Copilot Studio knows which physical resource holds your indexed policy documents, as distinct from any other Search service in the tenant.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/security-faq
https://learn.microsoft.com/en-us/microsoft-copilot-studio/knowledge-azure-ai-search

質問 # 124
Drag and Drop Question
A team has deployed an agent in Copilot Studio and needs to evaluate whether the agent is meeting its intended goals before expanding usage to a broader audience.
The team must evaluate agent performance using a structured and repeatable approach that produces actionable insights.
You need to select and apply an appropriate evaluation method by performing the required actions in the correct order.
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.

正解：

解説：

Explanation:
Step 1: Define the success criteria and evaluation objectives
You must always start by establishing what "success" looks like and what you are trying to measure. Without clear goals, you cannot choose the right metrics.
Step 2: Select the evaluation method and metrics aligned to the criteria Once goals are set, you determine how to measure them (e.g., using automated tests, human reviewers, or user satisfaction scores) and define specific key performance indicators (KPIs).
Step 3: Collect agent interaction data and evaluation signals
After setting up your metrics, you run the evaluation by gathering conversational logs, user feedback, and system performance telemetry during agent interactions.
Step 4: Review evaluation results and identify improvement opportunities Finally, you analyze the gathered data against your original criteria to pinpoint bottlenecks, conversational failures, or gaps, allowing you to iterate and improve the agent.

質問 # 125
A team is preparing to assess an agent in Copilot Studio before expanding access to additional users.
The team requires an evaluation that provides controlled, repeatable, and consistently measured test runs.
The evaluation must:
Be triggered directly by the team to control when testing occurs.
Determine success based on clearly established acceptance criteria.
Rely on a predefined set of inputs that ensures consistency across repeated runs.

正解：

解説：

Explanation:
Controlled trigger # Execute the evaluation on demand; Acceptance criteria # Compare responses against defined expectations; Consistent repeated inputs # Use a curated prompt list as the interaction source.
Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: An on-demand evaluation gives the team explicit control over when the test is executed, which is appropriate before widening access or immediately after a meaningful change. Defined expectations convert the business acceptance criteria into measurable pass/fail or score-based judgments instead of relying on informal review.
A curated prompt list supplies the same input cases for each run, eliminating variation that would arise from live user conversations. Scheduling an evaluation is useful for continuous monitoring but would not meet the stated requirement that the team trigger it directly. Live conversations are valuable for discovering new cases, yet they are not a stable regression set because their content and distribution change over time. The curated set should cover knowledge retrieval, tool use, safety boundaries, authentication-dependent behavior, and common failure paths. Expected answers and expected resources should be specific enough to identify regressions without penalizing valid paraphrases. Once the evaluation is run, the team should inspect both aggregate scores and individual activity details before deciding whether the agent is ready for broader use.
Study Guide alignment: Test and manage agents > Evaluate agent performance > Create a test set; Choose an evaluation method.

質問 # 126
......

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