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Microsoft GitHub Agentic AI Developer : GH-600 Exam

GH-600 actual test
  • Exam Code: GH-600
  • Exam Name: GitHub Agentic AI Developer
  • Updated: Aug 02, 2026
  • Q & A: 85 Questions and Answers
  • PDF Demo
  • PC Test Engine
  • Online Test Engine
  • Total Price: $59.99  

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Microsoft GH-600 Exam Syllabus Topics:

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

Microsoft GitHub Agentic AI Developer Sample Questions:

1. Drag and Drop Question
You have a GitHub repository that uses the GitHub Copilot coding agent to resolve issues and create draft pull requests. The repository uses GitHub Actions for CI, and reviewers rely on pull request timelines and workflow artifacts to understand what the agent did.
During long-running agent tasks, the reviewers lose track of decisions and validation steps, which causes repeated questions and reworks when context drifts between iterations.
You need to persist task progress and decisions as durable artifacts and ensure that the reviewers can verify what the agent did during and after execution by using GitHub as the system of record.
What should you do for each requirement? To answer, drag the appropriate actions to the correct requirements. Each action 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.


2. 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) Tool/terminal invocation permissions in agent mode
B) CODEOWNERS
C) .copilotignore
D) MCP server integration


3. An agentic coding session in the terminal has grown too long, and you want to discard all prior conversation history and start clean, without summarization. Which slash command should you run?

A) /usage
B) /reset-context
C) /compact
D) /clear


4. Hotspot Question
You have a GitHub repository that uses the following GrtHub Copilot CLI command in a Bash script.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.


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 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 tools: ['read', 'search'] in both agent profiles to prevent either agent from writing files.
C) 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.
D) Configure a concurrency group on both agent workflows so that only one workflow runs at a time.


Solutions:

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

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