Microsoft GitHub Agentic AI Developer : GH-600

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Microsoft GH-600 Exam Syllabus Topics:

Microsoft GitHub Agentic AI Developer Sample Questions:

1. You have a GitHub repository that uses GitHub Copilot code review on pull requests.
You plan to add repository-wide code review guidance that will apply to all files.
You need Copilot code review to consistently apply the guidance during pull request reviews.
What should you do?

A) Update the instructions in the .github/agents/*.agent.md files.
B) Modify the .github/pull_request_template.md file.
C) Create a .github/instructions/copilot.instructions.md file.
D) Create a .github/copilot-instructions.md file.

2. Drag and Drop Question
You have a GitHub repository that runs an agentic software development lifecycle (SDLC) workflow by using GitHub Actions. The workflow uses the following three executors implemented as scripts: spec_analyzer, risk_reviewer, and plan_merger.
You need to coordinate multiple specialized agents so that analysis and risk review run in parallel, and then a final executor merges the outputs into a single plan. The orchestration pattern must fan out one request to multiple executors, and then fan in the results to a final executor.
How should you complete the workflow definition? 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.

3. You want a reusable, shareable set of instructions that defines a specific persona/workflow (e.g.,
"security reviewer") that developers can invoke on demand in Copilot Chat, rather than instructions applied globally to every session. What should you create?

A) agents.md
B) .copilotignore
C) A prompt file (.prompt.md)
D) copilot-instructions.md

4. You have a GitHub Copilot coding agent that has completed a pull request for a security fix in your repository.
Before merging, you need to evaluate the quality of the agent's work by using both automated evaluation signals and human review.
You review the session log and the pull request.
What are two automated evaluation signals generated by the coding agent's built-in scanning tools? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A) the detection of hardcoded secrets, such as API keys and tokens
B) CodeQL findings that identify security vulnerabilities in the generated code
C) linting errors from the repository's CI pipeline configured in GitHub Actions
D) comments from Copilot code review that suggest improvements to code patterns
E) the median time to merge metric reported on the Copilot usage metrics dashboard

5. You have a private GitHub repository that has Copilot memory enabled.
Several developers who have write access to the repository make changes across multiple branches, including creating some pull requests that are later closed without merging.
Your team needs to understand how GitHub Copilot ensures that only task-relevant, up-to-date information influences code suggestions, even when older memories exist.
How does Copilot manage memories?

A) Copilot validates each memory's citations against the current branch before using the memory, and ignores the memory if the referenced code no longer exists.
B) Copilot stores memories per user, ensuring that only the developer who created a memory can trigger the memory in future sessions.
C) Copilot stores memories indefinitely until a repository administrator deletes them manually.
D) Copilot automatically blocks memory creation from pull requests that are closed without merging, to prevent outdated information from being stored.

Solutions: