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| Section | Weight | Objectives |
|---|---|---|
| Test, deploy, and monitor agentic AI systems | 20% | - Deploy and monitor agents at scale
|
| Integrate tools, data, and services | 25% | - Connect data sources and knowledge bases
|
| Implement agents and multi-agent systems | 30% | - Build agents with Azure AI tools and frameworks
|
| Design agentic AI solutions | 25% | - Define requirements for agentic systems
|
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NEW QUESTION # 14
You need to troubleshoot the issue reported by Dev1.
What should you review?
Answer: D
Explanation:
The agent session log is the appropriate starting point when the reported problem concerns an agent's interaction, execution sequence, or tool activity within the development environment. It provides information closer to the failing behavior than repository-level workflow usage statistics.
A useful investigation establishes what the agent was asked to do, which operations it attempted, what responses those operations returned, and where progress stopped or diverged from the intended result. Session diagnostics can help distinguish an instruction problem from an unavailable tool, a rejected operation, or an execution error. VS Code also provides detailed chat debugging facilities for inspecting interactions and diagnosing failures.
GitHub Actions usage metrics primarily describe workflow consumption. Runner logs are relevant when evidence points to a hosted workflow or runner problem. The token permissions block becomes relevant when the failure concerns authorization to a GitHub resource. None should be assumed to be the root cause before examining the reported execution.
The PDF selects B; the underlying Dev1 incident description is not included, so this selection assumes a session-level agent issue.
Study-guide topics: diagnostic evidence, tool-call inspection, and failure localization. Reference: VS Code-Debug chat interactions.
NEW QUESTION # 15
You have a GitHub Enterprise Cloud Organization that uses the GitHub Copilot coding agent.
Copilot creates a draft pull request for an assigned issue, and the pull request timeline shows Copilot started work.
After 70 minutes, the agent session log stops updating, and the pull request body status stops changing.
You need to restart the agent so that it continues the task from the issue context and produces new commits to the existing draft pull request.
What should you do?
Answer: D
Explanation:
To restart the background session and force the agent to resume its task, unassign the issue from GitHub Copilot and then reassign it to Copilot.
This specific operational cycle terminates the frozen cloud background process and launches a fresh agent session. Because a draft pull request already exists and is bound to the issue context, the newly initiated session automatically detects the linked branch, picks up the previous implementation plan, and begins pushing new commits directly to that existing draft PR.
Reference:
https://docs.github.com/en/copilot/how-tos/use-copilot-agents/cloud-agent/troubleshoot-cloud-agent
NEW QUESTION # 16
You are architecting an agentic AI system and need the agent's tool-calling behavior to be constrained so it can only call a specific allow-listed set of MCP tools, never arbitrary ones. What should you configure?
Answer: B
Explanation:
MCP client configurations (e.g., in VS Code settings or the CLI config) allow administrators to explicitly allow-list which MCP servers/tools an agent may invoke, restricting the attack surface and preventing unintended tool use.
NEW QUESTION # 17
You have a GitHub Enterprise Cloud repository that uses the GitHub Copilot coding agent.
Engineers assign issues to Copilot, and Copilot creates draft pull requests. The engineers start tasks either by assigning issues on github.com or by using GitHub Copilot Chat in an IDE. Reviewers request updates by leaving pull request comments.
You discover that sometimes, Copilot uses outdated requirements after a reviewer posts an updated instruction in a pull request comment, and, in several cases, Copilot fails to resume work from the comment.
You need to ensure that iteration requests are applied to the correct pull request session and are processed consistently.
What should you do?
Answer: C
Explanation:
The reviewer must have write access and explicitly mention @copilot in the relevant pull request comment. These requirements establish both authorization and routing. GitHub documents that Copilot responds to comments from users with write access, allowing an authorized reviewer to request further changes on the pull request being evaluated.
A normal comment can communicate information to other reviewers without becoming an instruction to the agent. The explicit mention distinguishes an actionable iteration request from surrounding discussion. The comment should clearly state the updated requirement and identify any earlier direction that it replaces.
Option A confuses the IDE's GitHub participant with the pull-request iteration mechanism. Option C restarts work through the issue assignment path rather than directly delivering the update to the existing pull request. Option D creates a separate task, introducing unnecessary context separation and potentially duplicating work.
After a valid request is accepted, the reviewer can check for the acknowledgment reaction and a new work event in the pull request timeline. These provide evidence that the instruction reached the agent.
Relevant curriculum topics are execution continuity, correct context routing, and preventing stale requirements during iterative work.
Reference:
NEW QUESTION # 18
You want to grant the Copilot coding agent access to only a narrowly scoped set of repository permissions (e.g., read code, write pull requests) rather than full admin access. What governs this scope?
Answer: B
Explanation:
The correct answer is the GITHUB_TOKEN permission configuration. GitHub uses the automatically generated GITHUB_TOKEN to authenticate workflow operations against the repository, and its authorization scope can be reduced through repository defaults and the workflow permissions key. Permissions can be set globally for a workflow or narrowed further at the individual job level. GitHub recommends granting only the minimum access necessary for the task.
For example, a workflow that only needs repository content access and pull-request modification can explicitly grant appropriate contents and pull-requests permissions rather than broad write access. Importantly, once individual permissions are specified, unspecified permission categories are set to none, providing a concrete least-privilege security boundary. Repository or organization policy can also establish restrictive default GITHUB_TOKEN permissions.
.copilotignore concerns which content Copilot should exclude, while copilot-instructions.md provides behavioral and coding guidance; neither is an authorization mechanism. /context reports information about the Copilot CLI context and does not control repository privileges.
Study Guide Reference Topics: Implement Guardrails and Accountability; least-privilege authorization; tool and execution permissions; repository security boundaries; controlled agent access.
NEW QUESTION # 19
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