GH-600 Exam Preparation Material | Developing in Agentic AI Systems

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Prepare for the GH-600 Developing in Agentic AI Systems exam with CertQueen's independently developed study resources. Review important concepts, practice scenario-based questions, and use clear explanations to identify areas that require further study.

Question#1

HOTSPOT
You have a GitHub Actions workflow that runs two jobs named agent-plan and agent-implement on pull requests.
The agent-plan job generates an implementation plan that downstream jobs must consume, but the job currently writes the plan only to the console.
You need to capture the implementation plan as a step output and configure the workflow so that downstream jobs can reference the output.
How should you configure the workflow? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Explanation:
Correct selection
Agent output capture method
Write plan=... to $GITHUB_OUTPUT.
Downstream reference expression
${{ needs.agent-plan.outputs.plan }}
GitHub Actions distinguishes console logs, environment variables, step outputs, and job outputs. Writing the implementation plan to the console makes it visible in logs but does not create an output that another job can reference. The producing step must write a named value to the file identified by GITHUB_OUTPUT.
The workflow must also expose that step output as a job output.
For example, a step with the identifier generate can publish plan, and the agent-plan job can map its outputs.plan property to ${{ steps.generate.outputs.plan }}. The consuming job declares needs: agent-plan and reads ${{ needs.agent-plan.outputs.plan }}. Both the output mapping and the dependency declaration are necessary for the intended cross-job contract.
The screenshot’s displayed GITHUB_ENV selection is incorrect. That mechanism makes environment variables available to subsequent steps in the same job; it does not publish a downstream job output. A multiline plan additionally requires the supported multiline output syntax.
The relevant curriculum topics are sharing agent state, maintaining execution continuity, and preserving decisions across workflow stages.
Reference: Passing information between jobs.

Question#2

HOTSPOT
Your company uses Microsoft Visual Studio Code and GitHub Copilot Chat.
You have a GitHub repository that uses main as the default branch. The repository contains a workspace custom agent stored at .github/agents/release-notes.agent.md.
A developer switches to a branch named branch1 where the agent file does NOT exist. In the same session, the developer switches to a user profile named profile1.profile1 contains a custom agent file named release-notes.agent.md that has user-invokable set to false.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Question#3

HOTSPOT
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. NOTE: Each correct selection is worth one point.

Explanation:
To draft and open the pull request, use
GitHub Copilot coding agent assigned to an issue
To automate the pull request review before the human review, use
GitHub Copilot code review on the pull request
The workflow requires two distinct capabilities: implementing the requested change and reviewing the resulting change. Assigning the issue to the Copilot coding agent supplies the implementation task. The agent can inspect the repository, modify code, add or update tests, and open a pull request for review. This matches the requirement to turn an assigned issue into a proposed implementation. GitHub―Use Copilot cloud agent on GitHub.
Copilot code review examines the pull request and produces review feedback. It therefore belongs after the implementation has produced a reviewable diff and before the workflow requests the intended human review. The automation must request or configure this review at the appropriate stage; assigning an issue alone does not establish the complete review sequence.
CI, automated code review, and human review provide complementary checks. CI validates executable requirements such as tests and lint rules. Automated review highlights potential code issues. Human reviewers retain responsibility for assessing the proposed change and satisfying applicable approval requirements. GitHub―Copilot code review.
Study-guide topics: agent integration into the SDLC, issue-to-pull-request workflows, automated validation, and human review.

Question#4

DRAG DROP
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. Each signal 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.

Question#5

Your GitHub Enterprise Cloud organization relies on GitHub Actions for CI/CD.
You plan to let an agent update and deploy workflows that access production environment secrets.
You must choose an autonomy level for a compliance-sensitive workflow.
Which autonomy level should you select?

A. fully autonomous execution without approvals

B. human-in-the-loop that requires manual approval for production changes

C. autonomous execution restricted to non-production environments

D. read-only analysis that cannot trigger any workflows

Exam Code: GH-600
Q & A: 109 Q&As Updated: Nov 12,2026

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What This GH-600 Study Resource Helps You Do

Review Key Concepts

Review the technologies, products, processes, and practical skills covered by the current GH-600 exam objectives.

Practice Scenario-Based Questions

Work through independently developed questions designed to strengthen your understanding of technical scenarios and decision-making.

Identify Knowledge Gaps

Use your results and the provided explanations to find weaker areas and focus your study more effectively.

How to Use This GH-600 Preparation Material

Review the Exam Scope

Start by reviewing the topics covered by the GH-600 exam. Compare them with the official exam objectives to understand the required technologies, operational tasks, and practical skills, then identify the areas that deserve the most attention.

Practice Independently

Complete a focused set of practice questions for each topic. On your first attempt, avoid referring to notes, answers, or other study resources so that you can evaluate your current understanding more accurately.

Study the Explanations

Review the answers and explanations after completing each practice session. Understand why the correct option is appropriate for the given scenario and why the other options may be incorrect or less suitable.

Close Knowledge Gaps

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Exam Code: GH-600
Q & A: 109 Q&As
Updated: Nov 12,2026