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| Section | Weight | Objectives |
|---|---|---|
| Test, deploy, and monitor agentic AI systems | 20% | - Validate agent performance and safety
|
| 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% | - Design agent architecture
|
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NEW QUESTION # 55
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues in an ephemeral GitHub Actions environment.
Builds fail unless an environment variable named NPM_TOKEN is available, because the repository depends on a private package registry.
A workflow named .github/workflows/copilot-setup-steps.yml was added but was merged into a non-default branch and does NOT run when new agent sessions start.
You need to ensure that for every new agent session, the setup steps run, and the private registry token is available as NPM_TOKEN, without hardcoding secrets in the repository.
What should you do?
Answer: C
Explanation:
The Copilot setup workflow must be present on the repository's default branch to be used for new coding-agent sessions. Moving .github/workflows/copilot-setup-steps.yml there ensures the setup procedure is discoverable for every newly created agent environment.
The package-registry credential should be stored as an environment secret in the Copilot environment and exposed using the required variable name, NPM_TOKEN. This permits package installation without committing the token into workflow YAML, instructions, source code, or pull requests.
Disabling network controls does not resolve missing credentials and expands risk unnecessarily. Repository instructions can describe setup expectations but cannot safely substitute for an injected runtime secret. Adding the raw token to YAML permanently exposes it to repository history and anyone with repository access.
Study-guide topics: ephemeral environments, setup workflows, environment secrets, and secure dependency access.
NEW QUESTION # 56
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: A
Explanation:
You should explicitly instruct the reviewer to mention @copilot in the pull request comments, and you must verify that they have write access to the repository.
Explicit Triggering: GitHub Copilot's asynchronous coding agent listens for active summons in existing pull request threads. Simply leaving a comment without an explicit @copilot mention may cause it to skip processing the update entirely, explaining why it fails to resume work.
Context Preservation: By directly mentioning @copilot within the specific pull request comment thread, the agent binds the new instructions directly to that exact active development session.
This prevents it from pulling outdated requirements from the initial issue definition.
Permission Enforcement: GitHub Copilot will ignore commands and mentions from users who do not possess write permissions (or higher) on the repository to prevent unauthorized code modifications or resource usage.
Reference:
https://github.blog/ai-and-ml/github-copilot/assigning-and-completing-issues-with-coding-agent-in-github-copilot/
NEW QUESTION # 57
You have a multi-agent GitHub Actions workflow that uploads review artifacts for each run.
You discover that some workflow run artifacts are being deleted manually.
You need to use your organization's audit log data to identify which user deleted the artifacts.
Which audit log search filter should you use?
Answer: B
Explanation:
The filter action:artifact.destroy selects the audit event associated with manual deletion of a workflow artifact. It directly targets the activity described in the scenario, allowing the investigator to examine the recorded actor and associated event details.
A repository filter narrows the search to a particular repository but does not distinguish artifact deletion from other recorded operations. It can usefully accompany the action filter when investigating a specific project. A workflow-run action concerns workflow execution rather than the manual removal of its stored artifacts. The generic operation:remove option does not select the documented artifact deletion event.
The investigation should correlate the deletion event with its timestamp and repository information, then inspect the actor recorded for that event. This identifies the account associated with the action; additional context may be needed when an application or automated identity performed it.
Artifact retention and audit logging serve separate purposes. Retention controls how long outputs are normally preserved, while the audit log records relevant administrative and user activity. Searching the audit log does not restore deleted content.
Study-guide topics: accountability, evidence preservation, actor attribution, and audit trails. Reference: GitHub-Organization audit log events.
NEW QUESTION # 58
You have a GitHub Enterprise Cloud organization that has Copilot agents enabled.
You discover activity in a repository.
You need to identify whether the activity was initiated by an agent.
Which two audit log fields can you use? Each correct answer presents a complete solution.
Answer: B,C
Explanation:
The agent_session_id field associates an activity with a particular agent session. It supports correlation of multiple events generated during the same agent-run lifecycle and helps investigators trace the origin of a change.
The actor_is_agent field identifies whether the event actor is an agent. This is the direct classification field for distinguishing agent-initiated activity from activity initiated by a human account or another type of actor.
The user field can identify an account associated with the event but does not independently establish that the action was performed by an agent. The action field identifies the recorded operation, such as a repository or workflow action, but does not itself identify the actor type.
Using both selected fields provides the most useful audit result: one confirms agent origin and the other supplies the session-level correlation needed to investigate the agent's activity sequence.
Study-guide topics: audit fields, agent attribution, session correlation, and accountability.
NEW QUESTION # 59
In Microsoft Visual Studio Code, you use GitHub Copilot agent mode for a repository. The GitHub Model Context Protocol (MCP) server is enabled.
Your team maintains an internal orchestrator script that creates independent Copilot SDK sessions to process operational events from webhooks.
You discover that the agent sometimes mixes context between unrelated webhook deliveries. Additionally, in some cases, the agent starts running tools before a human reviews the proposed steps.
You need to ensure that the script meets the following business requirements:
Planning must be kept separate from executing.
Each plan must be captured without calling any tools.
Each webhook event must create a new independent session.
Once the plan is complete, tool calls must be enabled within the same event-specific session for follow-up.
Answer:
Explanation:
NEW QUESTION # 60
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