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Updated: Nov 03, 2026
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Microsoft GH-600 Exam Overview:
| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Developing in Agentic AI Systems |
| Exam Number: | GH-600 |
| Exam Price: | USD 100 |
| Real Exam Qty: | 40β60 |
| Certificate Validity Period: | 2 years |
| Passing Score: | 700 |
| Exam Duration: | 120β150 |
| Exam Format: | Performance-based/lab tasks, Scenario-based, Multiple choice, Multiple select |
| Related Certifications: | Microsoft Certified: Azure AI Engineer Associate Microsoft Certified: Azure Developer Associate |
| Available Languages: | English |
| Recommended Training: | GH-600 Learning Paths on Microsoft Learn Semantic Kernel Documentation Azure AI Foundry Documentation |
| Exam Registration: | Pearson VUE Registration Microsoft GH-600 Exam Registration |
| Sample Questions: | Microsoft GH-600 Sample Questions |
| Exam Way: | Online proctored or Pearson VUE test center |
| Pre Condition: | Recommended: AZ-204 (Azure Developer Associate) or AI-102 (Azure AI Engineer Associate); proficiency in Python or C# and Azure development experience |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/certifications/exams/gh-600 |
Microsoft GH-600 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement agents and multi-agent systems | 30% | - Build agents with Azure AI tools and frameworks
|
| Topic 2: Design agentic AI solutions | 25% | - Define requirements for agentic systems
|
| Topic 3: Integrate tools, data, and services | 25% | - Incorporate external tools and APIs
|
| Topic 4: Test, deploy, and monitor agentic AI systems | 20% | - Validate agent performance and safety
|
Microsoft Developing in Agentic AI Systems Sample Questions:
Your company uses GitHub Copilot custom agents in Microsoft Visual Studio Code.
The company also uses the Copilot coding agent on GitHub issues.
You have a file named .planner.agent.md that defines an agent named planner. planner has tools set to [ ' search ' , ' read ' , ' fetch ' ]. There are explicit instructions NOT to write or modify any code. The file also defines a handoff labeled Start Implementation to an agent named implementer and sets send to false.
Developers report that after the planner agent produces a plan, implementation sometimes starts immediately in the same conversation and code changes appear without an explicit agent switch.
When Copilot-created pull requests stall, maintainers review the pull request timeline and session logs.
Several stalled sessions show outbound network commands blocked by a firewall, and the repositories do NOT contain a .github/copilot-instructions.md file.
For each statement, select Yes or No.
Correct Answer:
Explanation:
Statement
Selection
The configuration of the planner agent explicitly prevents code changes from being produced in the same conversation.
Yes*
The planner agent is violating its allowed tools by attempting internet access, and removing search will resolve the issue.
No
Adding a repository-level .github/copilot-instructions.md file that requires the agent to run tests and linters will mitigate the stalled pull request that has a failing CI anti-pattern.
Yes
*Interpreting "code changes" as direct file modifications by the correctly loaded planner, not code suggestions printed in chat or changes after switching agents.
The intended first selection is Yes: the planner has a read-oriented tool set, explicit planning-only instructions, and a handoff that does not automatically submit the implementation prompt. When this profile is correctly loaded and enforced, the planner lacks an editing tool for direct file modification. However, this does not prevent it from displaying proposed code in a response, nor does it permanently restrict another agent subsequently selected in the conversation. Unexpected file edits warrant checking the active profile and actual tool availability.
The second selection is No. The configured fetch capability permits network retrieval, so an internet-access attempt does not itself demonstrate tool misuse. Furthermore, firewall failures in the GitHub coding agent environment must be investigated in that environment; removing local search capability does not address their cause.
The third selection is Yes because repository instructions can specify the commands and expectations for testing and linting, helping reduce pull requests that fail CI. This mitigation does not independently resolve blocked network dependencies.
Relevant curriculum topics are planning/execution boundaries and diagnosis of agent anti-patterns.
References: VS Code custom agents , Copilot task best practices .
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?
- A. Create an organization-level Actions secret named NPM_TOKEN and reference it from .github/copilot- instructions.md.
- B. Disable the agent's integrated firewall and rename the workflow.
- C. Move .github/workflows/copilot-setup-steps.yml to the repository's default branch and store the token as a GitHub Actions environment secret named NPM_TOKEN in the Copilot environment.
- D. Add the token directly under env: npm_token: in the workflow file.
Correct Answer: C π³οΈ
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You want the GitHub Copilot coding agent to follow project-specific conventions (coding style, testing requirements, folder structure) on every task it performs in a repository. What should you create?
- A. A agents.yml workflow file
- B. A copilot-instructions.md file in .github
- C. A CODEOWNERS file
- D. A .copilotignore file
Correct Answer: B π³οΈ
Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues and create draft pull requests. The repository uses GitHub Actions for CI, and reviewers rely on pull request timelines and workflow artifacts to understand what the agent did.
During long-running agent tasks, the reviewers lose track of decisions and validation steps, which causes repeated questions and reworks when context drifts between iterations.
You need to persist task progress and decisions as durable artifacts and ensure that the reviewers can verify what the agent did during and after execution by using GitHub as the system of record.
What should you do for each requirement? To answer, drag the appropriate actions to the correct requirements. Each action 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:
Requirement
Correct action
Create durable run outputs that are downloadable after completion
Use the upload-artifact action and configure artifact retention in the CI workflow.
Provide ongoing, human-reviewable task progress signals inside the pull request Watch for pull request body updates and pull request timeline events.
Provide a real-time view of agent actions for audit and troubleshooting Select View session to stream live agent logs.
The requirements distinguish three forms of evidence: retained outputs, reviewable progress, and live execution details. Each serves a different purpose during an extended agent task.
The upload-artifact action preserves files generated by the workflow, such as validation reports, implementation plans, and decision records. Configuring retention determines how long those files remain available after execution. The workflow must actually generate and upload the relevant records; artifact storage does not automatically capture every agent decision. GitHub documents artifacts as the mechanism for storing and sharing workflow-produced data. GitHub Actions-Store and share data .
Pull request body updates and timeline events provide progress information where reviewers already evaluate the proposed change. They associate the evolving work with the pull request and help reviewers follow iterations without reconstructing the entire session.
Selecting View session exposes detailed agent activity during execution, supporting immediate troubleshooting and inspection. Assigning an issue starts work, while approving workflows authorizes CI execution; neither substitutes for the required evidence mechanisms. GitHub-Troubleshoot Copilot cloud agent .
Study-guide topics: durable task state, execution observability, artifact retention, and traceable progress.
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 artifact.destroy audit log event
- B. The pull request description and comment history
- C. The merge commit history
- D. The checks.delete_log audit log event
Correct Answer: A π³οΈ
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