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Microsoft GH-600 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare agent architecture and SDLC processes | 15-20% | - Configure observability and control for autonomous agents
|
| Topic 2: Perform evaluation, error analysis, and tuning | 15-20% | - Analyze agent failures and identify root causes
|
| Topic 3: Manage memory, state, and execution | 10-15% | - Persist agent state and manage context drift
|
| Topic 4: Implement guardrails and accountability | 10-15% | - Define autonomy levels
|
| Topic 5: Orchestrate multi-agent coordination | 15-20% | - Operate and manage multi-agent workflows
|
| Topic 6: Implement tool use and environment interaction | 20-25% | - Integrate agents within development environments
|
Microsoft Developing in Agentic AI Systems Sample Questions:
You have a GitHub Enterprise organization that has Copilot memory enabled.
You create a new repository.
What are two ways that memories will be deleted from the repository? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
- A. manually by repository members
- B. when the code that created the memory is deleted
- C. when pull requests are merged
- D. when the repository is archived
- E. automatically after 28 days
Correct Answer: A,E 🗳️
Explanation: Only visible for DumpsQuestion members. You can sign-up / login (it's free).
You want to prevent GitHub Copilot from ever suggesting completions or making edits inside a directory containing sensitive credentials templates. What should you configure?
- A. A repository ruleset
- B. A .copilotignore file
- C. A CODEOWNERS file
- D. Branch protection rules
Correct Answer: B 🗳️
Explanation: Only visible for DumpsQuestion members. You can sign-up / login (it's free).
You have a GitHub Enterprise Cloud repository that uses GitHub Actions for CI and requires pull requests for all changes.
You are planning a GitHub Actions workflow where a coding agent drafts implementation changes and tests from GitHub issues, and an automated review runs before human review.
You need the agent to draft changes from an assigned issue, open a pull request, and add an automated review to the pull request before requesting a human review.
What should you do? To answer, select the appropriate options in the answer area.
Correct Answer:
Explanation:
Requirement
Selection
To draft and open the pull request
GitHub Copilot coding agent assigned to an issue
To automate the pull request review before human review
GitHub Copilot code review on the pull request
Assigning an issue to the GitHub Copilot coding agent initiates the implementation workflow. The agent uses the issue as its task definition, works in a dedicated branch, prepares the code and tests, and opens a draft pull request. This creates a reviewable implementation boundary before changes reach the protected branch.
GitHub Copilot code review belongs on the resulting pull request because it evaluates the actual diff. Plan mode can help formulate an approach, but it neither opens a pull request nor performs an automated review of the implemented code. The correct sequence is issue assignment, implementation pull request, automated review, then human review.
This separates planning, coding, automated inspection, and human accountability. CI validates executable behavior, Copilot review identifies potential code-level concerns, and the human reviewer retains approval responsibility.
Study-guide topics: agent-driven SDLC workflows, pull-request automation, code review, and human approval.
You have a GitHub Enterprise Cloud organization that uses the GitHub Copilot coding agent to resolve issues asynchronously.
When an issue is assigned to GitHub Copilot, the agent creates a draft pull request, but your team cannot always tell whether the agent is actively working, has completed its session, or is awaiting workflow approval.
Which execution context does each signal indicate? To answer, drag the appropriate context to the correct signals.
Correct Answer:
Explanation:
Signal
Execution context
The eyes emoji reaction appears on the issue.
The agent acknowledges the assignment and will create the draft pull request.
The pull request timeline shows Copilot started work.
The agent session is actively running and generating live logs.
A draft pull request exists, but GitHub Actions checks are not running.
A human must manually approve and run the workflows.
The eyes reaction signals that Copilot has acknowledged the issue assignment. It is an early lifecycle indicator that the coding agent accepted the work and will begin its workflow.
A pull-request timeline event showing that Copilot started work indicates an active session. This is the appropriate place to observe ongoing status updates and session activity while the agent creates or revises the draft pull request.
When the draft pull request exists but its workflows have not started, the likely state is pending manual authorization. GitHub can require a user with write access to approve and run workflows associated with the pull request, especially where the workflow originates from an untrusted context or requires explicit approval.
These indicators are complementary: issue reactions establish acknowledgement, timeline events show active execution, and inactive checks can identify an approval gate rather than an agent failure.
Study-guide topics: asynchronous coding agents, execution signals, workflow approval, and pull-request observability.
You have a GitHub repository that uses a custom GitHub Copilot coding agent defined in the rollout-bot.
agent.md file.
You need to update a workflow so that agent-profile changes can be rolled back by reverting a single commit and rerunning the workflow. The workflow must check out the exact commit being deployed and apply the agent profile from the repository at that commit.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Target
Correct selection
Checkout ref
${{ github.sha }}
File checked by test -f
github/agents/rollout-bot.agent.md
Profile passed to the deployment script
github/agents/rollout-bot.agent.md
The workflow must deploy an agent profile from a specific repository snapshot. Setting the checkout reference to ${{ github.sha }} identifies the commit associated with the workflow event, rather than following a branch reference that might move as new commits arrive. The checkout action supports selecting a commit SHA through its ref input.
Both subsequent operations must use .github/agents/rollout-bot.agent.md. The existence check confirms that the expected profile is present in the checked-out revision, and the deployment script receives that same version-controlled file. Using a different path would break the relationship between the validated profile and the deployed profile.
Rollback requires one additional operational distinction: reverting a change creates a new commit containing the restored content. The deployment must run against that revert commit. Re-running the original failed workflow run retains its original SHA and does not automatically deploy the later revert.
This approach keeps agent behavior, repository history, and deployment provenance aligned, making the profile's effective version identifiable and recoverable.
Relevant curriculum topics are durable state, controlled rollback, and continuity of agent configuration across executions.
References: actions/checkout , Re-running workflows and jobs .
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