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| Section | Objectives |
|---|---|
| Topic 1: Governance, Security, and Compliance | - Environment management in Power Platform - Managing access control and authentication - Data protection and responsible AI practices |
| Topic 2: Integrate External Systems and Data Sources | - Connecting Azure services and APIs - Using Power Automate for workflow orchestration - Integrating Dataverse and Power Platform components |
| Topic 3: Deploy, Monitor, and Optimize AI Agents | - Monitoring performance and analytics - Publishing and deploying copilots - Iterative improvement and lifecycle management |
| Topic 4: 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 |
| Topic 5: 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 |
Jpexamは生徒を常に惹きつけ、Microsoft熱心な顧客からの世界的なフィードバックの進歩に情熱を移します。AB-620試験で彼らが夢をかなえるためにこの分野でナンバーワンであることを証明します。 AB-620試験問題の質の高さを保証しているため、AB-620練習教材はより優れた教育効果をもたらします。 また、学習の後方情報の蓄積が生徒に大きな負担を感じさせる代わりに、最新のAB-620試験ガイドは、あらゆる種類の生徒の有効性または正確性のニーズを満たすことができます。
質問 # 20
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 deploy the Blue Yonder Copilot agent to the public website and Microsoft Teams while ensuring compliance with the company's security and Responsible AI requirements.
Which two actions should you perform before making the agent available on both channels? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
正解：B、D
解説：
[A]
You can configure prompt modifications at the system and agent level in Microsoft Copilot Studio to satisfy your organization's Responsible AI (RAI) guidelines.
[E]
Scenario:
Compliance and security
Responsible AI content moderation filters must be enabled.
Reference:
https://microsoft.github.io/agent-academy/operative/06-ai-safety/
質問 # 21
You deploy an agent in Microsoft Copilot Studio that triggers a cloud flow to update customer records in Microsoft Dataverse.
You observe the following after deploying the agent:
Several runs fail due to a temporary authentication issue.
The flow continues to trigger and generate additional failed runs.
You must:
正解：
解説：
Explanation:
Stop further failures # Turn off the flow; Reprocess the corrected execution # Resubmit the failed run; Verify recent success # Review Activity history.
Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: Turning off the flow is the safest immediate control when a temporary authentication failure is causing repeated triggered runs. It prevents additional failures and noise while the connection is repaired without deleting the flow or altering its trigger logic. After credentials or connection references are corrected, resubmitting the failed run reprocesses the original input and is more precise than creating an unrelated new execution. The Activity history then provides run-level evidence that recent executions succeeded, including status, timestamps, action outputs, and error details. Analytics summaries are useful for trends but are less appropriate for immediate confirmation of a specific recovery. Cancelling only the latest run would not stop future triggers, and recreating or republishing the flow is unnecessary when the underlying design is sound. The flow should be re- enabled only after the connection is validated. Administrators should document the incident and consider alerting, bounded retries, and explicit error handling so a transient dependency does not generate an uncontrolled series of failures again. Study Guide alignment: Plan and configure agent solutions > Create and monitor agent flows in Copilot Studio > Monitor agent flows.
質問 # 22
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 # 23
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：
質問 # 24
An agent's flow-based tool intermittently fails when passed nested JSON objects as input parameters. What is the most likely cause?
正解：C
解説：
Power Automate flow triggers used as agent tools generally expect flat, well-defined parameter schemas; complex nested objects often need to be flattened, serialized as a string, or parsed with a Parse JSON action inside the flow.
質問 # 25
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