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| Section | Weight | Objectives |
|---|---|---|
| Test, deploy, and manage agents | 20–25% | - Test and validate agent performance
|
| Plan and configure agent solutions | 30–35% | - Plan an agent solution
|
| Build and extend agents in Copilot Studio | 40–45% | - Implement advanced capabilities
|
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NEW QUESTION # 121
Drag and Drop Question
A company uses an agent in Copilot Studio to answer questions based on enterprise content.
The content is indexed in Azure AI Search. Responses must be generated by a Microsoft Foundry model.
To meet the business needs, the solution must meet the following requirements:
- Users are able to trace each answer back to its source.
- The agent retrieves information from a centralized data index.
- Responses are generated by an enterprise-approved foundation model.
You need to configure generative answers for the agent.
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:
Explanation:
Box 1: Instruct the agent to cite sources in knowledge settings
The specific behavior of how the agent presents that information-such as enforcing citations and tracing links back to the source documents-is controlled by the instructions and configurations within the agent's knowledge and conversation settings. You must explicitly configure the agent's prompt instructions or system settings to format the output with source citations so users can trace the answers.
Box 2: Configure Azure AI Search as the grounding data source
By adding Azure AI Search as a knowledge source Microsoft Copilot Studio enables the Retrieval-Augmented Generation (RAG) pattern. This ensures your generative AI model retrieves specific enterprise content from your centralized index and uses it to ground responses.
Box 3: Deploy a model in Foundry to an agent and connect to Copilot Studio Model Control: To strictly guarantee that an enterprise-approved foundation model (such as a specific Microsoft Foundry model) generates the response, you must manually deploy that specific model and link it to your agent.
Connection Workflow: Deploying the custom model in Azure AI Studio (formerly encompassing parts of Azure AI Foundry) and connecting it to Copilot Studio ensures the orchestration layer routes prompts directly to your certified LLM.
Reference:
https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/ai-search
NEW QUESTION # 122
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 # 123
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.
Answer: A,E
Explanation:
* B. Configure prompt instructions that include disclaimer text. Centralized prompt instructions apply the required disclaimer and behavioral controls consistently to generative responses. This avoids maintaining duplicate text in individual topics and reduces the risk of inconsistent compliance language.
Microsoft confirms that prompt modifications can control response content, format, personality, and what the agent should or should not answer. Microsoft Learn: Prompt modification
* C. Use Markdown syntax within response content. The scenario explicitly requires Markdown for formatting and states that HTML is unsupported. Markdown can provide headings, bold text, lists, and other structured presentation while remaining compatible with the target channels.
Options A and E duplicate disclaimer content across topics, creating unnecessary maintenance and inconsistent responses when the disclaimer changes. Option D violates the stated platform requirement because HTML must not be used.
NEW QUESTION # 124
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 implement tool usage in topics that meet the Blue Yonder design and governance requirements.
Which implementation method should you use for each requirement? To answer, move the appropriate implementation methods to the correct requirements. You may use each implementation method 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:
Explanation:
Box 1: Add a Question node for the user to choose the tool.
Let the agent decide when to run a tool based on user input.
Box 2: Add an Adaptive card
Run a specific agent flow at a defined step in a topic.
You can run a specific agent flow at a defined step in your topic by using an Ask with Adaptive card node to collect user input, and then triggering the flow using the submitted data.
Box 3: Add a tool node
Use a connector action to retrieve data during a conversation.
Reference:
https://learn.microsoft.com/en-us/microsoft-copilot-studio/authoring-ask-with-adaptive-card
NEW QUESTION # 125
Drag and Drop Question
A company needs an agent to answer questions using live enterprise records and to execute a limited set of operations in the same system.
To meet the business needs, the agent must meet the following requirements:
- The agent must update a record using authenticated access.
- The agent must answer questions using live data at runtime.
- The agent must avoid indexing enterprise data into a search index.
You need to select the correct Power Platform connector approach for each requirement.
Which option should you use for each requirement? To answer, move the appropriate solutions to the correct requirements. You may use each solution 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:
Explanation:
Box 1: Power Platform connector tool.
Performing active operations/actions (like updates, creates, or deletes) in external systems requires using connectors configured as Tools (actions/plugins) within Microsoft Copilot Studio.
Actions execute operations on behalf of the user or system with explicit authentication.
Box 2: Power Platform connector for real-time knowledge.
Adding a Power Platform connector directly as a real-time knowledge source allows Copilot Studio to query external system APIs dynamically at runtime to retrieve live data for answering queries.
Box 3: Power Platform connector for real-time knowledge.
Unlike Graph connectors or standard knowledge indexing (which preprocesses and stores data into a search index), real-time knowledge queries data on-the-fly via API calls without ingesting or pre-indexing the source records into a separate search index.
Reference:
https://learn.microsoft.com/en-us/microsoft-365/copilot/extensibility/overview-copilot-connector
NEW QUESTION # 126
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