Free AB-620 Exam Braindumps
Designing and Building Integrated AI Agent Solutions in Copilot Studio

Case Study:
This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
To answer the case study questions, you will need to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.
To start the case study
To display the first question in this case study, select the "Next" button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions.
When you are ready to answer a question, select the "Question" button to return to the question.
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.

This Exam Topic does not have a case study.

Case Study:
This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
To answer the case study questions, you will need to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.
To start the case study
To display the first question in this case study, select the "Next" button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions.
When you are ready to answer a question, select the "Question" button to return to the question.
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.

Case Study:
This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
To answer the case study questions, you will need to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.
To start the case study
To display the first question in this case study, select the "Next" button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions.
When you are ready to answer a question, select the "Question" button to return to the question.
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.

This Exam Topic does not have a case study.

This Exam Topic does not have a case study.