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A solution architect is implementing a governance process for an agent developed in Copilot Studio. Before the agent is moved to production, the organization requires a standardized validation process to ensure that updates to the underlying knowledge base do not cause regressions in response accuracy.
The validation process must satisfy the following technical criteria:
Consistency: Execute a fixed library of test cases containing specific user prompts.
Grounding: Measure the quality of the generated output against a verified "golden" response.
Comparison: Provide a performance delta between the current version and previous test runs.
You need to choose an evaluation method that meets the requirements.
What should you use?
Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: A predefined test set with expected outcomes is the only option that satisfies consistency, grounding comparison, and repeatable regression analysis. The fixed prompts ensure that each version receives the same inputs. Verified expected responses provide the baseline against which meaning, quality, keywords, or exact output can be scored. Rerunning the same set produces comparable results over time, allowing the team to calculate whether a knowledge-base change improved or degraded performance. The manual test pane is valuable for interactive debugging and inspecting the activity map, but it does not provide the same controlled batch or historical comparison. Channel usage metrics describe adoption and operational behavior, not response correctness. Transcript keyword searches can reveal patterns after deployment but do not establish a golden answer or a controlled experiment. The evaluation method should match the content: compare-meaning for legitimate paraphrases, exact match for fixed values, keyword match for required terms, or a custom rubric for domain-specific quality. Results should be retained or exported when a longer audit history is required. Study Guide alignment: Test and manage agents > Evaluate agent performance > Create a test set; Choose an evaluation method; Review test results.
Users can add content to a paragraph field in a team-managed project, but they cannot @mention other users.
Identify the cause.
Comprehensive and Detailed Explanation From [Atlassian Jira Cloud Documentation]
The cause is the Jira Browse users and groups global permission. Atlassian states that users with this permission can view user names when working with user-picker fields or @mentioning users. Without it, Jira cannot provide the affected users with the complete user directory required to search for and select another person through an @mention.
The fact that users can enter and save ordinary text in the paragraph field confirms that the field is present, editable, and available on the applicable work-item layout. Therefore, field settings and screen settings are not responsible. Notification settings determine how users receive updates after an event and do not control whether names appear in the mention picker.
Project permissions and issue-level security can affect whether a particular person can access the project or work item. However, a general inability to search for or mention other users indicates that the initiating users lack the site-wide user-browsing capability.
A Jira administrator should review Settings System Global permissions and grant Browse users and groups to an appropriate group containing the affected users, subject to the organization's privacy policies.
Relevant documentation topic: Jira Cloud administration Global permissions Browse users and groups. See What are global permissions, and what do they control?.
A company is configuring an agent in Copilot Studio that uses a generative answers node inside multiple topics.
The company requires responses to meet the following requirements:
Must use an executive summary format.
Must apply only within a specific topic.
Must continue using configured knowledge sources.
Must comply with connector security controls.
You need to configure a custom prompt to meet the formatting requirements.
Solution: Remove the generative answers node and rely only on the custom prompt to produce formatted responses.
Does the solution meet the goal?
Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: Removing the generative answers node eliminates the component that searches the configured knowledge sources and produces the grounded topic response. A standalone custom prompt can format or transform supplied input, but it does not automatically inherit the topic's selected knowledge sources or reproduce the node's retrieval behavior. The resulting answer could therefore have the requested executive-summary style while failing the grounding requirement. The correct design retains the generative answers node and applies topic-scoped custom instructions to its response, or captures the grounded output in a variable and sends that content through a carefully configured formatting prompt. General knowledge should remain disabled when the topic must use only approved enterprise sources. Connector security and data policies continue to govern the actual source connections and cannot be replaced by prompt text. The proposed removal is consequently not a valid solution. Validation should inspect the retrieved sources, generated answer, final format, and behavior when no authorized source returns relevant content. Study Guide alignment: Plan and configure agent solutions > Configure topics > Configure generative answers node; Configure advanced agent responses with custom prompts.
You are designing an agent for external customers that must comply with responsible AI principles. The agent will make recommendations about financial products. What is a critical responsible AI consideration you must address in your agent design?
Responsible AI for financial recommendations requires transparency (users must understand how recommendations are derived), grounding (citations of data sources), confidence indicators, and guardrails (disclaimers and constraints preventing the agent from acting as a fiduciary or making guarantees). This reduces harm and builds trust. Using the most advanced model without constraints does not address responsibility. Enabling the agent to give legal financial advice creates liability. Disabling audit logging violates governance and transparency principles.
An agent uses a flow that calls an external service which can occasionally fail or time out.
When a failure occurs, the agent must meet the following requirements:
Must not terminate silently.
Must send a notification containing the error details.
You need to configure the agent flow so that failures are handled in a controlled and predictable way.
Solution: Configure run after conditions for failure paths.
Does the solution meet the goal?
Comprehensive and Detailed Explanation From Microsoft AB-620 Study Guide: Configure run after is the control that routes execution to an error-handling action when the external-service action fails, times out, is skipped, or reaches another selected status. By configuring the notification action to run after failure and timeout, the flow no longer terminates silently and can send a message containing sanitized error context. The handler can then set a deliberate flow status, record a correlation ID, and provide the operator with the failed action and remediation path. This design should be paired with an appropriate retry policy based on whether the external operation is safe to repeat. For non-idempotent writes, automatic retry may create duplicates and should generally be disabled or protected with an idempotency key. Error notifications must not include tokens, secrets, or unrestricted payloads. Testing should deliberately produce an authorization failure, timeout, and service error to prove that each status enters the handler and that the notification itself has a fallback if its connector is unavailable. Study Guide alignment: Plan and configure agent solutions > Create and monitor agent flows in Copilot Studio > Implement error handling in agent flows.
94 questions covering all exam domains, starting from $20
Exam domains verified against: Official Microsoft AB-620 exam guide, last checked August 2026.
Design agents for internal or external audiences while planning integration with enterprise systems and identity strategy. Evaluate security and governance considerations alongside responsible AI strategy and plan reusable agent components and deployment channels.
Sample question from this domain above: Q3
Build agent flows including human-in-the-loop scenarios, configure topics with advanced responses and custom prompts, and manage variables and error handling. Add input and output parameters and implement generative answers with adaptive cards and API integrations.
Sample question from this domain above: Q4
Add agent flows to topics, configure response formatting and tools, and manage generative answers with custom knowledge sources. Implement advanced configurations with custom prompts and adaptive cards.
Connect to enterprise knowledge sources including Azure AI Search and Copilot connectors. Configure MCP tools and computer use, then implement multi-agent collaboration using Foundry agents, Fabric data agents, and the A2A protocol.
Integrate with Azure AI Search, Microsoft Power Platform connectors, and Copilot connectors. Configure generative answers and custom prompts with Foundry model catalog support.
Sample question from this domain above: Q2
Configure and monitor computer use capabilities for agents. Add REST APIs, custom connectors, and Model Context Protocol tools to extend agent functionality.
Sample question from this domain above: Q5
Design multi-agent solutions and integrate Foundry agents, Fabric data agents, and existing agents. Create solutions using the Agent2Agent (A2A) protocol for cross-platform orchestration.
Sample question from this domain above: Q1
Configure generative answers using Azure AI Search with Foundry and custom prompts with Foundry model catalog. Monitor agents using Application Insights for performance tracking.
Evaluate agent performance by creating test sets and choosing evaluation methods, then review results. Implement application lifecycle management including solution creation, environment variables, and Microsoft Power Platform Pipelines extension.
Create and execute test sets for agents using appropriate evaluation methods. Review test results to validate agent behavior and performance before production deployment.
Create solutions and add existing agents to them while managing environment variables. Implement and extend Microsoft Power Platform Pipelines for deployment automation.
Common questions about the exam itself