GH-600 Q&As - in .pdf

GH-600 pdf
  • Exam Code: GH-600
  • Exam Name: GitHub Agentic AI Developer
  • Updated: Aug 25, 2026
  • Q & A: 85 Questions and Answers
  • PDF Price: $59.99
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  • Exam Code: GH-600
  • Exam Name: GitHub Agentic AI Developer
  • Updated: Aug 25, 2026
  • Q & A: 85 Questions and Answers
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GH-600 Testing Engine
  • Exam Code: GH-600
  • Exam Name: GitHub Agentic AI Developer
  • Updated: Aug 25, 2026
  • Q & A: 85 Questions and Answers
  • Software Price: $59.99
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Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Orchestrate multi-agent coordination15–20%- Design workflows for multiple agents
- Prevent conflicts and manage shared resources
- Monitor and troubleshoot multi-agent execution
- Define communication and handoff protocols
Implement guardrails and accountability10–15%- Log actions, decisions, and changes for audit
- Add validation, review, and approval gates
- Ensure compliance, safety, and responsible use
- Enforce least privilege and security boundaries
Implement tool use and environment interaction20–25%- Configure and extend GitHub Copilot agents
- Connect agents to codebase, APIs, and external systems
- Manage permissions and environment access
- Implement tools, custom actions, and MCP servers
Perform evaluation, error analysis, and tuning15–20%- Diagnose failures, hallucinations, and unexpected behavior
- Optimize prompts, tools, and behavior through iteration
- Define metrics and quality standards for outputs
- Test, validate, and compare agent results
Prepare agent architecture and SDLC processes15–20%- Design agent autonomy and decision boundaries
- Define agent purpose, scope, and success criteria
- Integrate agents into software development lifecycle
- Plan agent deployment, monitoring, and maintenance
Manage memory, state, and execution10–15%- Implement memory cleanup and expiration rules
- Choose memory types: short-term, long-term, external
- Scope and persist agent state correctly
- Handle execution flow, retries, and interruptions

Microsoft GitHub Agentic AI Developer Sample Questions:

1. You are troubleshooting why a Copilot coding agent pull request keeps failing CI checks after every attempted fix. What is the most effective first step?

A) Add clear, reproducible steps and expected behavior to the issue
B) Increase MCP server rate limits
C) Enable Copilot memory
D) Switch the agent to --allow-all mode


2. A designated top-level instructions file is used by some agentic tools to describe overall repository purpose, build/test commands, and conventions in a tool-agnostic way (usable across multiple AI coding agents, not just Copilot). What is this file commonly called?

A) copilot-instructions.md
B) copilot-setup-steps.yml
C) agents.md
D) .copilotignore


3. Hotspot Question
You have a GitHub Enterprise organization that uses GitHub Copilot.
You discover that GitHub Copilot Chat responses in Microsoft Visual Studio Code are influenced by earlier, unrelated troubleshooting prompts from the same conversation.
You need to ensure that the Copilot Chat conversation context is limited to information relevant to the current work item. The solution must minimize effort.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


4. 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

Drag and Drop Question
You need to implement the security requirements for agent1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.


5. You have a GitHub Enterprise Cloud Organization that uses a custom coding agent to run GitHub Actions workflows that create branches, open pull requests, and merge changes after required checks pass.
You need to log all agent-initiated actions and ensure that the logs are retained for two years.
What should you do?

A) Enable Git events in the audit log.
B) Enable log forwarding.
C) Configure audit log retention.
D) Configure audit log streaming to Azure Event Hubs.


Solutions:

Question # 1
Answer: A
Question # 2
Answer: C
Question # 3
Answer: Only visible for members
Question # 4
Answer: Only visible for members
Question # 5
Answer: D

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