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Failing GH-600 means paying the full registration fee a second time, and that is an expensive way to learn. Prepare with 85 Microsoft GitHub Agentic AI Developer practice questions from Fast2test and give yourself the best chance of passing on your first attempt.
Microsoft GH-600 Exam Overview:
| Certification Vendor: | GitHub |
|---|---|
| Exam Name: | GitHub Certified: Agentic AI Developer |
| Exam Number: | GH-600 |
| Available Languages: | English |
| Exam Duration: | 120 minutes |
| Exam Format: | Interactive Exam Components, Proctored Exam, Scenario-based Questions |
| Certificate Validity Period: | 24 months |
| Related Certifications: | GitHub Copilot GitHub Advanced Security GitHub Certified Foundations GitHub Actions |
| Sample Questions: | Microsoft GH-600 Sample Questions |
| Exam Way: | Pearson VUE testing center or online proctored exam. |
| Pre Condition: | No formal prerequisites. Candidates should have experience with SDLC workflows, GitHub controls, GitHub Copilot, MCP servers, agent customization, code quality, security, and review practices. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/agentic-ai-developer/ |
Microsoft GH-600 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare agent architecture and SDLC processes | 15-20% | - Integrate agents into the software development lifecycle
|
| Topic 2: Orchestrate multi-agent coordination | 15-20% | - Coordinate multiple agents
|
| Topic 3: Implement guardrails and accountability | 10-15% | - Implement governance controls
|
| Topic 4: Perform evaluation, error analysis, and tuning | 15-20% | - Evaluate agent performance
|
| Topic 5: Manage memory, state, and execution | 10-15% | - Manage context and memory
|
| Topic 6: Implement tool use and environment interaction | 20-25% | - Manage execution environments
|
Microsoft GH-600 FAQs: What Every Candidate Asks
The GH-600 exam, officially titled GitHub Agentic AI Developer, is the qualifying test for the GitHub Administrator certification from GitHub, a credential at the Intermediate level. Passing it proves you have the skills employers look for in certified professionals, and it can also support progress toward related credentials such as GitHub Certified Foundations, GitHub Actions, GitHub Advanced Security, GitHub Copilot.
No formal prerequisites. Candidates should have experience with SDLC workflows, GitHub controls, GitHub Copilot, MCP servers, agent customization, code quality, security, and review practices. Eligibility rules can change over time, so before you register, confirm the latest requirements on the official exam page: GitHub GH-600 exam overview.
Yes. Fast2test offers a free PDF demo for the Microsoft GitHub Agentic AI Developer exam so you can judge the quality of our questions and answers before paying anything. Every purchase also comes with 365 days of free updates, and once that period expires you can extend your update service at a 50% discount.
Your purchase is protected by a 100% money-back guarantee. If you sit the corresponding GH-600 exam within 60 days of buying and do not pass, send us a scan of your exam enrollment slip together with your official Score Report (PDF) within 2 days of the exam date — the candidate name must match the payer's name — and we will process your full refund within 7 days. Please note that exams taken within 3 days of purchase, materials downloaded without ever sitting the exam, free products, and expired orders are not covered. If you would rather not have a refund, you can exchange your order for two exam products of equal value, free of charge, and keep the update service on your original purchase.
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The Microsoft GitHub Agentic AI Developer syllabus is organized into 6 domains. The main areas include Implement guardrails and accountability (10-15%), Orchestrate multi-agent coordination (15-20%), and Prepare agent architecture and SDLC processes (15-20%). Scroll up to the Exam Topics section above for the complete, current outline before you plan your study schedule.
Microsoft GitHub Agentic AI Developer Sample Questions:
Hotspot Question
You have a GitHub repository that uses the following GrtHub Copilot CLI command in a Bash script.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Explanation:
Box 1: Yes
Setting --max-autopilot-continues 10 acts as a hard ceiling, preventing infinite loops by cutting off the execution the moment it reaches the 10-step limit.
Box 2: Yes
This command will allow the agent to use all local tools without prompting you for permission.
The --yolo flag is a built-in alias in the official GitHub Copilot CLI. It bypasses safety confirmation prompts by combining three specific permission-granting arguments: --allow-all-tools, --allow-all- paths, and --allow-all-urls.
Box 3: No
This specific command will not allow targeted human intervention at key decision points because it explicitly strips away all prompt checkpoints The options configuration used in your script forces the GitHub Copilot CLI to bypass user confirmation entirely and execute the objective fully autonomously Reference:
https://pub.towardsai.net/i-stopped-prompting-github-copilot-and-started-delegating-to-it-fe2f12a21709?gi=fbf268b2a564
https://docs.github.com/en/copilot/concepts/agents/copilot-cli/autopilot
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?
- A. Ensure that the reviewer has write access to the repository. Instruct the reviewer to mention
@copilot in the pull request comments. - B. Start a new task from Copilot Chat in the IDE. Instruct the reviewer to mention @github in the task comments.
- C. Ensure that the agent has write access to the repository. Instruct the reviewer to unassign and reassign the original issue to Copilot.
- D. Ensure that the agent has write access to the repository. Instruct the reviewer to mention @github in the task comments.
Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).
You have a GitHub Enterprise repository that runs an autonomous agent by using a GitHub Actions workflow. The workflow has the following jobs:
- agent-run that generates trace.json and plan.md
- review that waits for human approval before continuing
- deploy that uses the outputs from agent-run
You need to make the files inspectable in the GitHub Actions UI and ensure that the files are available to the review and deploy jobs.
What should you do in the workflow?
- A. Store trace.json and plan.md on a network share and have later jobs retrieve them from the share.
- B. Upload trace.json and plan.md as workflow artifacts in agent-run, and download the files inside the jobs.
- C. Use dependency caching to store trace.json and plan.md.
- D. Commit trace.json and plan.md back to the repository from agent-run.
You use a GitHub Actions workflow to orchestrate a multi-agent draft-review process for pull requests. The workflow uploads a single combined review-package artifact at the end of each run.
You discover that the review-package artifact is occasionally missing from the workflow run history, and you suspect that a user deleted it.
You need to identify which user manually deleted a workflow run artifact and when the deletion occurred.
What should you use?
- A. the checks.delete_logs audit log event
- B. the artifact.destroy audit log event
- C. the merge commit history
- D. the pull request description and comment history
Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).
Hotspot Question
You have a GitHub repository that contains the following custom agent files:
- A file named planner.agent.md that includes YAML frontmatter with a
handoffs entry that has label: Start Implementation, agent:
implementer, and prompt: Now implement the plan outlined above
- A file named implementer.agent.md that is in the same directory as
planner.agent.md and includes YAML frontmatter that has name:
IMPLEMENTER
You add a third agent file named review.agent.md.review.agent.md includes YAML frontmatter that has name: code-review.
You make the following changes to planner.agent.md:
- Update the existing handoff to include send: true and model: GPT-5.2
(copilot).
- Add a second handoff that has label: Run Review, agent: code-review,
and prompt: Review the code changes made in the previous step.
No other agent files are modified.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Explanation:
Box 1: Yes
GitHub Copilot Extensions and agent frameworks route handoffs by matching the agent identifier in the YAML frontmatter to the filename or the name property defined within that repository.
Box 2: Yes
The failure happens because the agent target pointer in the handoff configuration does not match the actual target agent's declaration or file name due to a mismatch introduced by your changes.
Box 3: Yes
Yes, selecting "Start Implementation" will switch the chat to the implementer agent and automatically submit the prompt using the specified model.
The updates made to your GitHub agent repository configure a seamless workflow transition.
Here is how your new settings process that action:
Trigger action: Clicking Start Implementation activates the specific handoff block you modified.
Agent switch: The agent: implementer property targets the file with name: IMPLEMENTER, routing the conversation there.Automated submission: Setting send: true forces the interface to submit the text immediately instead of leaving it in the text box.
Model override: The model: GPT-5.2 (copilot) line forces the system to process that automated submission using that specific model version.
Reference:
https://arinco.com.au/blog/github-copilot-multi-repo-instructions/
https://aminrj.com/posts/building-a-multi-agents-coding-workflow/
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