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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 Format: | Multiple choice, Performance-based/lab tasks, Scenario-based, Multiple select |
| Passing Score: | 700 |
| Exam Duration: | 120–150 |
| Exam Price: | USD 100 |
| Real Exam Qty: | 40–60 |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Microsoft Certified: Azure AI Engineer Associate Microsoft Certified: Azure Developer Associate |
| Available Languages: | English |
| Recommended Training: | Azure AI Foundry Documentation Semantic Kernel Documentation GH-600 Learning Paths on Microsoft Learn |
| Exam Registration: | Microsoft GH-600 Exam Registration Pearson VUE 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: Test, deploy, and monitor agentic AI systems | 20% | - Validate agent performance and safety
|
| Topic 2: Integrate tools, data, and services | 25% | - Connect data sources and knowledge bases
|
| Topic 3: Design agentic AI solutions | 25% | - Define requirements for agentic systems
|
| Topic 4: Implement agents and multi-agent systems | 30% | - Build agents with Azure AI tools and frameworks
|
Microsoft Developing in Agentic AI Systems Sample Questions:
You have a GitHub Enterprise Cloud repository that uses the GitHub Copilot coding agent to resolve backlog issues by creating draft pull requests. The repository uses a GitHub Actions workflow for deployments to the production environment.
You discover that the workflow is being triggered before human review.
You need to configure GitHub controls to meet the following requirements:
* The workflow for the agent's draft pull requests must NOT run until a user that has write access approves the pull requests.
* Production deployment jobs must start only after a user with write access explicitly approves the jobs.
Which controls should you configure? To answer, drag the appropriate controls to the correct requirements.
Correct Answer:
Explanation:
Requirement
Control
Agent's draft pull request workflow control
Approve and run the workflows from the pull request merge box.
Production deployment job control
In the deployment job, configure the required reviewers for the production environment.
Approving and running workflows from the pull request interface controls whether workflows associated with the agent's draft pull request are allowed to begin. This provides the required human authorization before CI executes in that context.
Required reviewers on the production environment protect the deployment job itself. When the workflow reaches that environment, GitHub pauses the job until an authorized reviewer explicitly approves it. The approval is therefore applied at the production boundary, independently from the earlier pull-request workflow authorization.
A concurrency group controls overlap but does not require human approval. A custom deployment protection rule may be useful for specialized external validation, but it is not required by the stated scenario. Commit signing and run-summary inspection do not control the requested execution gates.
Study-guide topics: approval gates, protected environments, draft pull-request workflows, and production accountability.
You have a GitHub repository that uses GitHub Actions for CI on pull requests. The repository contains a Node.js app.
You have a GitHub Copilot coding agent that opens pull requests for backlog items, and your company requires automated checks for agent-generated changes.
You plan to standardize success criteria so that pull requests created by agents only succeed when unit tests pass and CodeQL analysis completes.
You need to configure a GitHub Actions workflow that runs on pull requests, executes unit tests, and performs CodeQL analysis.
How should you complete the workflow? To answer, drag the appropriate values to the correct targets.
Correct Answer:
Explanation:
Target
Value
Run tests command
npm test
Prepare CodeQL action
init
Run CodeQL action
analyze
The Node.js unit-test command is npm test, which invokes the project's configured test script. This supplies the functional validation component of the pull-request workflow.
CodeQL requires initialization before analysis. The github/codeql-action/init action prepares CodeQL for the languages and source code being evaluated. The github/codeql-action/analyze action then performs analysis and publishes the resulting security findings.
build is not the requested unit-test command, although some projects may need an additional build step.
upload-sarif is not the normal replacement for the CodeQL analyze action in a standard CodeQL workflow because analysis produces and submits the results.
The completed workflow should run under the pull_request event and grant the security-events permission needed to upload CodeQL results.
Study-guide topics: automated testing, CodeQL analysis, pull-request checks, and quality gates.
In Microsoft Visual Studio Code, you are using GitHub Copilot Chat to generate documentation for a new feature.
Earlier in the day, you used Copilot Chat extensively for an unrelated refactoring task.
You discover that the Copilot responses for the new documentation task are influenced by the earlier conversation.
You need Copilot to focus only on the current task and avoid using prior conversational context. The solution must NOT affect other conversations.
What should you do?
- A. Clear the chat history of the current conversation.
- B. Modify the Chat > Agent: Max Requests setting.
- C. Use @file to restrict the Copilot context to the currently open file.
- D. Change from sidebar chat to inline chat.
- E. Start a new Copilot Chat conversation.
Correct Answer: E 🗳️
Explanation: Only visible for PracticeMaterial members. You can sign-up / login (it's free).
You need to resolve the issue of the agents generating conflicting output. The solution must meet the implementation guidelines.
What should you do?
- A. Add shared/config.yaml to a CODEOWNERS file that requires SG_Review approval before any changes can be merged.
- B. Configure a concurrency group on both agent workflows so that only one workflow runs at a time.
- C. Configure tools: [ ' read ' , ' search ' ] in both agent profiles to prevent either agent from writing files.
- D. Configure each agent to work on a separate branch and add a required status check that detects file- level overlap before either pull request can be merged.
Correct Answer: D 🗳️
Explanation: Only visible for PracticeMaterial members. You can sign-up / login (it's free).
You have a GitHub repository that uses the GitHub Copilot coding agent. The repository uses strict branch protections on the main branch.
You maintain a long-lived branch named release/1.4 that has different dependencies and CI checks.
You need the agent to create a hotfix that is implemented and validated against the release/1.4 branch. The solution must prevent the agent from basing its work on main.
What should you do?
- A. In .github/copilot-instructions.md, instruct the agent to always check out release/1.4 instead of main when creating hotfixes.
- B. Add concurrency: release/1.4 to .github/workflows/copilot-setup-steps.yml.
- C. Require human reviews on changes to main.
- D. Select the repository and the release/1.4 branch in the Copilot agents UI before you run the agent task.
Correct Answer: D 🗳️
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