Free Practice Questions for Microsoft GH-600 Exam

Unassigning and reassigning the issue is GitHub’s documented retry action for an issue-driven agent session that remains stuck and times out. The troubleshooting guidance states that a persistently stuck session times out after an hour. At 70 minutes with no continuing activity, the scenario therefore calls for a fresh execution trigger rather than indefinite waiting.

Reassignment reissues the task through its original issue context. This makes C the appropriate selection among the available actions. The issue should contain current requirements so that the retry begins with the intended scope rather than repeating an outdated assignment.

Option B is incorrect because a draft pull request should not be merged simply to restart implementation, and Copilot does not respond to new mentions on merged or closed pull requests. Option D authorizes repository workflow execution rather than restarting the agent.

For operational precision, reassignment is a documented retry mechanism, not a universal guarantee that every retry reuses the same pull request. Where preserving a particular pull-request branch is essential, an authorized @copilot comment on that open pull request directly targets it.

Relevant curriculum topics are timeout recovery, task reinitialization, and preserving execution continuity.

Human-in-the-loop execution provides the appropriate autonomy level because the agent’s actions affect production deployment and access to production secrets. The agent can prepare changes and perform permitted validation, while an authorized person approves the production transition. This retains useful automation while placing human judgment at the consequential execution boundary.

GitHub Actions environments support required reviewers. A job referencing a protected environment must satisfy its protection rules before proceeding. GitHub explicitly documents that when approval is required, the job cannot access environment secrets until a required reviewer approves it. The approval gate therefore controls an operational capability, rather than merely asking the model to behave cautiously.

Option A removes the oversight required by the scenario. Option B prevents the agent from performing the requested deployment work. Option D restricts execution to non-production environments and consequently does not provide a controlled route to production.

The workflow should bind deployment to the protected production environment and align reviewer permissions with organizational policy. Relevant curriculum topics are risk-based autonomy levels, explicit authorization for compliance-sensitive changes, and least-privilege execution.