Copilot Studio Generative Answer Disclaimer Strategy

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. 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 ensure that every AI-generated response from the agent in Copilot Studio includes a disclaimer that complies with the company's security and governance policies. Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.