- Exam Code: GH-600
- Exam Name: GitHub Agentic AI Developer
- Updated: Aug 02, 2026
- Q & A: 85 Questions and Answers
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| Section | Weight | Objectives |
|---|---|---|
| Manage memory, state, and execution | 10–15% | - Implement memory cleanup and expiration rules - Handle execution flow, retries, and interruptions - Scope and persist agent state correctly - Choose memory types: short-term, long-term, external |
| Implement guardrails and accountability | 10–15% | - Ensure compliance, safety, and responsible use - Enforce least privilege and security boundaries - Add validation, review, and approval gates - Log actions, decisions, and changes for audit |
| Perform evaluation, error analysis, and tuning | 15–20% | - Optimize prompts, tools, and behavior through iteration - Define metrics and quality standards for outputs - Diagnose failures, hallucinations, and unexpected behavior - Test, validate, and compare agent results |
| Prepare agent architecture and SDLC processes | 15–20% | - Define agent purpose, scope, and success criteria - Design agent autonomy and decision boundaries - Integrate agents into software development lifecycle - Plan agent deployment, monitoring, and maintenance |
| Orchestrate multi-agent coordination | 15–20% | - Define communication and handoff protocols - Design workflows for multiple agents - Monitor and troubleshoot multi-agent execution - Prevent conflicts and manage shared resources |
| Implement tool use and environment interaction | 20–25% | - Manage permissions and environment access - Configure and extend GitHub Copilot agents - Implement tools, custom actions, and MCP servers - Connect agents to codebase, APIs, and external systems |
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) Modify the .github/pull_request_template.md file.
B) Create a .github/instructions/copilot.instructions.md file.
C) Create a .github/copilot-instructions.md file.
D) Update the instructions in the .github/agents/*.agent.md files.
2. Drag and Drop Question
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.
3. 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_logs audit log event
4. While using agent mode in VS Code, you want Copilot to run a specific test suite as a validation step after making changes, without manually invoking the terminal each time. What feature enables this?
A) .copilotignore
B) CODEOWNERS
C) Tool/terminal invocation permissions in agent mode
D) MCP server integration
5. Case Study 2
Existing Environment
GitHub Environment
The GitHub environment contains the following:
- Three repositories named product-api, billing-service, and infra-terraform.
- Branch protection on the main branch in all repositories that requires at least one pull request review before merging
- GitHub Actions runners used across all workflows
- A GitHub team named SG_Dev that contains developers
- A GitHub team named SG_Review that contains senior engineers and a security team
- A .github/copilot-instructions.md file that includes general coding conventions for all features Agent environment The product-api repository uses a GitHub Copilot coding agent named agent1 that has the following configurations:
- No custom agent profile is defined.
- A Model Context Protocol (MCP) server named MCP1 is deployed to
https://mcp.litwareinc.internal and provides access to internal ticketing and deployment APIs.
MCP1 requires an API key for authentication.
A second Copilot coding agent named agent2 handles changes in infra-terraform and runs in parallel with agent1 when both agents have open assigned issues.
Copilot memory is NOT enabled for the organization.
Problem Statements
Litware identifies the following issues:
- During two recent sessions, agent1 accessed files in billing-service, which is outside the agent's intended scope.
- agent1 makes code changes immediately after receiving a task.
- A developer named Ben, who is on the SG_Dev team, reports that agent1 completed a session with a successful status and opened a pull request, but the pull request contains no file changes.
Other developers report this intermittently as well.
- Both agent1 and agent2 modified shared/config.yaml in a parallel test run, generating conflicting outputs.
agent1 consistently uses raw try-catch blocks for error handling, which violates the defined implementation guidelines of SG_Dev.
Requirements
Planned Changes
Litware plans to make the following changes:
- Ensure that agent1 can access all the tools in the environment.
- Provide product-api with specific instructions to agent1 without affecting Copilot Chat or Copilot code review.
- Configure MCP1 as a tool for agent1 by modifying the product-api repository MCP configuration.
- Ensure that Copilot retains details that it has learned and uses that knowledge for future work.
This must be applied to all licensed members of the organization.
Implementation guidelines
The development team at Litware identifies the following implementation guidelines:
- Agent workflows must be able to run in parallel.
- Application error handling must use the repository ErrorHandler class.
- agent1 and agent2 must run on isolated branches during parallel execution. File-level conflicts must be detected before merges, and both agents must be able to run concurrently.
Security requirements
Litware identifies the following security requirements:
- Only the members of SG_Review must be able to approve agent1 plan outputs.
- All API keys must be stored and accessed securely.
- The developers must NOT be able to self-approve.
Agent configuration
You need to troubleshoot the issue reported by Ben.
What should you review?
A) the GITHUB_TOKEN permissions block in the agent1 workflow
B) the GitHub Actions usage metrics of repo1
C) the GitHub Actions runner log for the session job
D) the agent session log in the Agents panel
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: Only visible for members | Question # 3 Answer: A | Question # 4 Answer: C | Question # 5 Answer: D |
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