Microsoft Developing in Agentic AI Systems GH-600 Exam Questions

Answer : B

A comment mentioning @copilot on the existing pull request provides the direct continuation mechanism. GitHub documents that this can start a new agent session and, by default, push additional commits to the pull request's branch. The comment should explicitly identify the latest repository guidance and the work that remains, correcting the context that produced the outdated proposal.

This approach preserves the existing branch, accumulated changes, review discussion, and relationship to the original issue. It also gives the agent a clear instruction tied to the artifact that must be updated. The commenter must have the required repository write access.

Creating another issue would establish a separate assignment and could duplicate work. Approve and run workflows authorizes GitHub Actions validation; it does not itself resume the coding agent's implementation session. Closing and reopening the issue is likewise not the direct pull-request continuation control.

The useful recovery action therefore combines a new execution trigger with an explicit statement of current intent. Restarting without correcting the outdated requirement could reproduce the same drift.

Relevant curriculum topics are resuming execution, restoring task context, and maintaining continuity without discarding prior work.

===============

Answer : A, D

GitHub Copilot repository memories can be removed manually and through automatic retention cleanup. Repository owners or administrators can review repository-level facts under the repository's Copilot Memory settings and delete stored facts that are obsolete, misleading, or no longer appropriate. This makes A a valid deletion mechanism.

D is also correct. GitHub applies an inactivity-based retention policy: a stored repository fact or user preference that goes unused for 28 days is automatically deleted. The 28-day period can restart when Copilot successfully validates and uses that memory, so this should be understood as an unused-memory expiration period rather than an unconditional deletion exactly 28 days after creation.

Deleting the source code referenced by a memory does not directly delete that memory. Repository-level facts retain citations and are validated against the current branch before use; if the supporting information is no longer valid, Copilot does not use the fact. Likewise, archiving a repository or merging a pull request is not documented as an automatic memory-deletion event.

Study Guide Reference Topics: Manage Memory, State, and Execution; repository memory lifecycle; memory validation; memory retention and deletion; persistent agent knowledge.

Answer : B

The correct answer is Edit mode. GitHub Copilot's Edit mode is designed for situations where the developer wants granular control over which files may be changed and what modifications should be applied. In Edit mode, you explicitly select the working set of files, provide natural-language instructions describing the required changes, and then review the proposed edits before accepting or discarding them. GitHub describes Edit mode as appropriate for quick, specific updates to a defined set of files and for scenarios where the developer wants tighter control over the editing process.

Agent mode differs because Copilot determines which files and tools are required, can execute terminal commands, and iterates autonomously toward completing the task. Ask mode is intended primarily for explanations, questions, and code suggestions rather than coordinated file modification. Plan mode generates an implementation strategy before execution and is appropriate when the approach must be reviewed before coding begins.

Therefore, where the requirement explicitly emphasizes selecting specific files and prescribing precise edits rather than delegating scope determination to the agent, Edit mode provides the correct level of developer control.

Study Guide Reference Topics: Prepare agent architecture and SDLC processes; selecting appropriate Copilot interaction modes; controlled code modification; human-directed versus autonomous execution.

Answer : B

A job-level concurrency group coordinates executions that share the same group value across workflow runs. The expression ci-${{ github.ref }} places executions for the same ref into the same group while allowing other refs to use independent groups.

Under the default concurrency behavior, a group permits at most one running execution and one pending execution. When another execution enters an occupied group, it becomes pending; an existing pending execution can be replaced by the newer one. This is a concurrency limit rather than a guarantee that every queued execution will eventually run.

The strategy.max-parallel setting controls simultaneous jobs generated by a matrix within a workflow run. It does not establish the required coordination between separate workflow runs. A matrix definition also does not create a shared concurrency lock. The cancel-in-progress property controls whether an active execution is cancelled, but a cancellation expression alone does not define the necessary group.

Group names should be chosen carefully when multiple jobs or workflows share a repository, because identical names can coordinate those executions too.

Study-guide topics: job synchronization, concurrency groups, and bounded execution. Reference: GitHub Actions---Workflow syntax: job concurrency.

===============

Answer : D

The tasks.md file is the execution-tracking document for the upgrade scenario. It records individual tasks, their validation criteria, and their completion status. Microsoft's upgrade walkthrough specifically directs users to review this file when checking the status of every upgrade step.

A task can include several concrete verification activities, such as restoring dependencies, building the solution, correcting compilation errors, running the test suite, and rerunning tests after fixes. Reviewing these entries reveals whether implementation has merely been attempted or has satisfied the defined checks.

The assessment identifies upgrade issues and scope; it does not serve as the live record of completed work. A plan describes intended actions and ordering, which also differs from evidence that execution and testing succeeded.

The phrase ''before App1 is upgraded'' requires temporal care. Before execution, tasks.md identifies the steps and checks that must be performed. It cannot prove that future tests have already passed. Completion and test results become available as the agent executes and updates the tasks.

Study-guide topics: execution validation, task status, and acceptance criteria. Reference: Microsoft Learn---Execute and verify a Copilot upgrade.

===============

Answer : C

The correct solution combines a branch protection rule with a CODEOWNERS file. CODEOWNERS allows the repository to associate specific paths with designated users or teams. For example, entries can assign security or platform reviewers to .github/workflows/* and /infra/*. When a pull request modifies those paths, GitHub automatically identifies the corresponding code owners.

The enforcement mechanism comes from branch protection on main. Configure the protection rule to Require a pull request before merging and enable Require review from Code Owners. GitHub then blocks the merge until an applicable code owner approves the affected files. This converts CODEOWNERS from simple reviewer routing into an enforceable merge-control boundary.

agents.md and copilot-instructions.md provide behavioral guidance to AI agents; they do not enforce reviewer authorization. .copilotignore likewise does not establish mandatory merge approval. Although GitHub rulesets can also implement review controls, none of the ruleset choices provides the required CODEOWNERS pairing.

Study Guide Reference Topics: Implement Guardrails and Accountability; protected branches; required human review; CODEOWNERS; repository governance; approval gates.

Answer : A

Audit log streaming is the correct choice because it exports audit events to an external destination where the organization can apply its required two-year retention policy. This captures agent-initiated activity as part of the organization's auditable GitHub events.

Standard audit-log retention in GitHub may not meet a long-term evidence requirement on its own. Streaming allows the organization to store and search the exported events in a SIEM, data lake, or logging platform with retention controls aligned to compliance policy.

Log forwarding is not the required GitHub audit mechanism in this scenario. Enabling Git events can increase event visibility, but it does not independently provide the requested long-term retention of all relevant agent-initiated actions.

The external destination should preserve actor details, timestamps, repository identifiers, action types, and correlation information needed to reconstruct agent activity during an investigation.

Study-guide topics: accountability, audit log streaming, retention, and agent activity traceability.