Microsoft GH-600 : Developing in Agentic AI Systems

GH-600 real exams

Exam Code: GH-600

Exam Name: Developing in Agentic AI Systems

Updated: Sep 23, 2026

Q & A: 111 Questions and Answers

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If the GH-600 exam feels intimidating, start small: download the free demo from RealVCE, read a few sample questions from the Microsoft Developing in Agentic AI Systems collection, and see how much of it you already understand. Most candidates find the exam's reputation is scarier than its content — and 111 practice questions are there to close whatever gap remains.

Microsoft GH-600 Exam Overview:

Certification Vendor:Microsoft / GitHub
Exam Name:Developing in Agentic AI Systems
Exam Number:GH-600
Exam Duration:120 minutes
Passing Score:700/1000
Exam Format:Scenario-based questions, Interactive items, Proctored exam, Multiple-choice questions
Certificate Validity Period:1 year
Real Exam Qty:Approximately 40–60
Related Certifications:GitHub Certified: Agentic AI Developer
Exam Price:$165 USD
Available Languages:English
Sample Questions:Free Download real GH-600 VCE file
Exam Way:Proctored exam delivered through Pearson VUE; online and test-center delivery options are available.
Pre Condition:No formal prerequisite certification is specified. Candidates should have experience with the software development lifecycle (SDLC), GitHub workflows and controls, code quality, security and review practices, and coding agents such as GitHub Copilot, MCP servers, custom instructions, custom agents, tools, and Copilot setup steps.
Official Syllabus URL:https://learn.microsoft.com/en-us/credentials/certifications/agentic-ai-developer/

Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Implement tool use and environment interaction20–25%- Configure MCP servers
  • 1. Configure MCP allow lists
    • 2. Add an MCP server as a tool to an agent
      • 3. Configure a GitHub remote MCP server
        • 4. Configure MCP registries
          - Integrate agents within development environments
          • 1. Configure an agent to use branch-based scope
            • 2. Evaluate the execution context for an agent
              • 3. Configure an agent to handle environment-specific constraints
                • 4. Enable an agent to perform autonomous actions, including creating branches and pull requests
                  • 5. Configure an agent's scope to a specific repository
                    • 6. Configure an agent to be invoked in a CI workflow
                      - Operate agents with safe execution paths and robust error handling
                      • 1. Implement error handling
                        • 2. Implement retries
                          • 3. Implement escalation paths
                            • 4. Implement rollbacks
                              • 5. Implement traceability and accountability for agent actions
                                - Select and configure agent tools
                                • 1. Configure agent tool permissions
                                  • 2. Identify required tools
                                    • 3. Configure agent tools
                                      Orchestrate multi-agent coordination15–20%- Configure observability for multi-agent behavior by using logs, artifacts, and operational signals
                                      • 1. Configure multi-agent workflows to produce artifacts suitable for review and audit
                                        • 2. Perform post-hoc analysis of multi-agent behavior
                                          • 3. Document key decisions, handoffs, and outcomes across agents
                                            - Manage the lifecycle of agents within multi-agent workflows
                                            • 1. Update, reconfigure, or replace agents without disrupting active workflows
                                              • 2. Retire agents while preserving auditability and workflow continuity
                                                • 3. Add agents to existing multi-agent workflows
                                                  - Operate and manage multi-agent workflows
                                                  • 1. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs
                                                    • 2. Apply an orchestration pattern to coordinate multiple agents
                                                      • 3. Configure agent isolation for parallel execution
                                                        - Detect and respond to multi-agent failures and degraded behavior
                                                        • 1. Implement multi-agent recovery patterns, including rollback and human-in-the-loop
                                                          • 2. Respond to degraded behavior or coordination across agents
                                                            • 3. Identify failed, partial, or stalled agent executions
                                                              Manage memory, state, and execution10–15%- Ensure continuity of agent memory and state across tools and environments
                                                              • 1. Share agent state
                                                                • 2. Prevent conflicting context
                                                                  • 3. Prevent stale context
                                                                    - Persist agent state and manage context drift
                                                                    • 1. Detect and correct drift during extended agent execution
                                                                      • 2. Capture task progress and decisions as durable artifacts
                                                                        • 3. Resume agent work without repeating steps or diverging from prior decisions
                                                                          - Implement agent memory strategies
                                                                          • 1. Define memory expiration, pruning, and reset rules
                                                                            • 2. Choose between short-term, long-term, and external memory
                                                                              • 3. Scope agent memory to task-relevant information
                                                                                Prepare agent architecture and SDLC processes15–20%- Configure observability and control for autonomous agents
                                                                                • 1. Plan and implement the degree of agent autonomy, including guardrails
                                                                                  • 2. Configure agents to produce inspectable artifacts within standard development tooling
                                                                                    • 3. Configure human intervention for autonomous agents without slowing delivery
                                                                                      - Integrate agents into the software development lifecycle (SDLC)
                                                                                      • 1. Identify steps for agents to perform
                                                                                        • 2. Define inputs, outputs, and success criteria for agents
                                                                                          • 3. Identify and mitigate common anti-patterns in agents
                                                                                            - Define boundaries between planning, reasoning, and action
                                                                                            • 1. Prevent agent action until the agent checks and approves
                                                                                              • 2. Validate agent plans
                                                                                                • 3. Configure an agent to output a structured plan
                                                                                                  • 4. Configure agent planning to be distinct from agent execution
                                                                                                    Perform evaluation, error analysis, and tuning15–20%- Define success criteria and evaluation signals for agent tasks
                                                                                                    • 1. Specify expected outcomes and operational constraints for agent tasks
                                                                                                      • 2. Identify qualitative and quantitative evaluation signals to evaluate agents
                                                                                                        • 3. Generate evaluation signals by using automated scanning tools
                                                                                                          • 4. Align evaluation criteria with development intent
                                                                                                            - Analyze agent failures and identify root causes
                                                                                                            • 1. Classify root causes, including reasoning errors, tool misuse, and context or environment issues
                                                                                                              • 2. Identify failures by using logs, plans, traces, outputs, and workflow artifacts
                                                                                                                - Tune agent behavior based on evaluation results
                                                                                                                • 1. Revise instructions, workflows, or constraints
                                                                                                                  • 2. Refine memory usage
                                                                                                                    • 3. Refine tool usage and tool access
                                                                                                                      Implement guardrails and accountability10–15%- Implement guardrails and human-in-the-loop workflows
                                                                                                                      • 1. Identify the subset of actions that require human judgment
                                                                                                                        • 2. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes
                                                                                                                          • 3. Preserve execution velocity by minimizing approvals that do not materially reduce risk
                                                                                                                            • 4. Scope permissions and execution contexts to enforce least-privilege access
                                                                                                                              • 5. Block actions that violate defined security, compliance, or Responsible AI policies
                                                                                                                                - Define autonomy levels
                                                                                                                                • 1. Classify agent actions by operational, security, and compliance risk to right-size human interventions
                                                                                                                                  • 2. Assign autonomy levels to maximize delivery speed while remaining compliant with organizational security and Responsible AI standards

                                                                                                                                    GH-600 Exam Basics: What Every Candidate Asks

                                                                                                                                    Why is the GH-600 exam worth taking?

                                                                                                                                    The GH-600 exam is the required test for earning the Microsoft Developing in Agentic AI Systems certification from Microsoft. It assesses your command of the official exam objectives through scenario-based and knowledge questions, and the resulting credential is widely recognized by employers. Its reputation for difficulty is real but manageable — candidates who practice consistently with quality materials routinely walk in well prepared.

                                                                                                                                    What are the main topic areas of the GH-600 exam?

                                                                                                                                    The official outline for the GH-600 exam highlights these domains:

                                                                                                                                    • Manage memory, state, and execution (10–15%)
                                                                                                                                    • Prepare agent architecture and SDLC processes (15–20%)
                                                                                                                                    • Orchestrate multi-agent coordination (15–20%)

                                                                                                                                    Seeing the topics laid out this way often shrinks the exam's intimidation factor — each domain is a finite, learnable block, and the Microsoft Developing in Agentic AI Systems practice questions at RealVCE follow the same structure.

                                                                                                                                    What are the prerequisites for the GH-600 exam?

                                                                                                                                    No formal prerequisite certification is specified. Candidates should have experience with the software development lifecycle (SDLC), GitHub workflows and controls, code quality, security and review practices, and coding agents such as GitHub Copilot, MCP servers, custom instructions, custom agents, tools, and Copilot setup steps.

                                                                                                                                    Is the GH-600 exam really as difficult as people say, and how do I deal with that?

                                                                                                                                    The GH-600 exam is demanding, but its difficulty is specific, not mysterious: unfamiliar question formats, time pressure, and a few heavily weighted domains. All three respond to the same remedy — repeated, timed exposure to exam-style questions. That is what the Microsoft Developing in Agentic AI Systems practice materials at RealVCE provide, and the free demo lets you measure the real difficulty yourself before committing, which is usually the moment the fear starts shrinking.

                                                                                                                                    How many questions does the GH-600 exam have, and what is the time limit?

                                                                                                                                    The GH-600 exam consists of Approximately 40–60 questions with a time allowance of 120 minutes minutes. Practicing full sets under a similar time cap is the most direct way to make sure pacing never costs you points on exam day.

                                                                                                                                    Why should I start with the free demo instead of buying directly?

                                                                                                                                    Because seeing beats guessing. The free demo of the GH-600 exam materials at RealVCE contains genuine samples from the full Microsoft Developing in Agentic AI Systems question set — same format, same expert-verified answers — and downloading it costs nothing. If you are unsure whether the materials match your level or your study style, the demo answers that question with evidence rather than marketing, and every demo on the site is free of charge.

                                                                                                                                    What is the GH-600 exam's passing score and registration fee?

                                                                                                                                    The passing score is 700/1000 and the exam fee is $165 USD. Knowing both numbers early helps you plan: aim to be consistently above the passing mark in timed practice sessions before you spend the fee on a booking.

                                                                                                                                    Microsoft Developing in Agentic AI Systems Sample Questions:

                                                                                                                                    Question #1

                                                                                                                                    You need to implement agent2 to meet the technical requirements.
                                                                                                                                    How should you complete the YAML configuration? To answer, select the appropriate options in the answer area.
                                                                                                                                    NOTE: Each correct selection is worth one point.
                                                                                                                                    name: implementation-planner
                                                                                                                                    description: Creates detailed implementation plans and technical specifications in markdown format tools: [
                                                                                                                                    <Dropdown 1>,
                                                                                                                                    <Dropdown 2>,
                                                                                                                                    'microsoftdocs/mcp/docs_search',
                                                                                                                                    'microsoftdocs/mcp/docs_fetch'
                                                                                                                                    ]
                                                                                                                                    The accompanying image includes empty dropdown controls and recreated practice alternatives.

                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer:


                                                                                                                                    Topic 1, Contoso Ltd,
                                                                                                                                    Overview
                                                                                                                                    Contoso Ltd. is a software development company located in the United States.
                                                                                                                                    Existing Environment
                                                                                                                                    GitHub Environment
                                                                                                                                    Contoso uses GitHub Enterprise and assigns GitHub Copilot Pro+ licenses to its developers. The developers use Microsoft Visual Studio Code as their IDE.
                                                                                                                                    Contoso has a customer portal. The code for the portal is stored in a GitHub repository named repo1 that contains the following:
                                                                                                                                    A custom agent named agent1 that includes instructions to review specs related to best practices A custom instruction file named validate-instructions.md that is used to validate tone of voice and applies to all .md and .txt files A custom instruction file named codereview.instructions.md that is used by the Copilot coding agent but is excluded for use by the Copilot code review repo1 has the following structure:
                                                                                                                                    The front-end is stored in the /frontend folder.
                                                                                                                                    The API logic is stored in the /api folder.
                                                                                                                                    Contoso has a second repository named repo2 that contains a legacy .NET application named App1 built by using .NET 6. repo2 has a multi-agent workflow for modernization tasks.
                                                                                                                                    Contoso enables the Model Context Protocol (MCP) registry and allows the Microsoft Learn MCP Server. Every developer must configure their own connection to the Learn MCP Server.
                                                                                                                                    Problem Statements
                                                                                                                                    The developers working in repo1 report that the Microsoft Learn documentation is NOT being retrieved when they attempt to validate a design by using agent1.
                                                                                                                                    The testing team at Contoso identifies that the customer portal uses inconsistent UI styles, which leads to customer confusion and branding issues. The UI inconsistencies stem from variations in the folder structure.
                                                                                                                                    Agent Logs
                                                                                                                                    You have the following logs for the multi-agent workflow used in repo2.
                                                                                                                                    Requirements
                                                                                                                                    Planned Changes
                                                                                                                                    Contoso plans to have all agents and developers in repo1 use the Microsoft Learn MCP to ensure that reviews are validated by using the appropriate documentation. This must be implemented centrally.
                                                                                                                                    Contoso plans to leverage AI-powered coding agents to implement new portal features and pages.
                                                                                                                                    Technical Requirements
                                                                                                                                    App1 must be upgraded to .NET 10. A previous upgrade attempt was started by using the Copilot modernization agent, but the attempt was never finalized.
                                                                                                                                    You plan to retry the upgrade. You must first analyze App1 by using AI, and then generate a report that contains breaking changes and deprecated patterns before retrying the upgrade.
                                                                                                                                    All AI-generated code for UI styling must adhere to a predefined folder structure.
                                                                                                                                    The architects at Contoso need help building implementation plans for repo1. The company wants to implement a new agent named agent2 to analyze the code base and the code requirements, and then respond with a detailed plan. The agent must NOT be able to edit files or run local commands.
                                                                                                                                    The developers must be able to delegate work to the Copilot coding agent by assigning issues to the agent.

                                                                                                                                    Question #2

                                                                                                                                    You have a GitHub repository that uses three GitHub Copilot coding agents named agent1, agent2, and agent3.
                                                                                                                                    During structured evaluation runs, agent1 frequently returns Markdown narratives instead of a machine-parsable result.
                                                                                                                                    You need to ensure that agent1 consistently returns only a predefined JSON structure without affecting the output of the other agents.
                                                                                                                                    Which file should you modify?

                                                                                                                                    • A. .github/instructions/agent1.instructions.md
                                                                                                                                    • B. .github/agents/agent1.agent.md
                                                                                                                                    • C. .github/copilot-instructions.md
                                                                                                                                    • D. .github/workflows/copilot-setup-steps.yml
                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer: B  🗳️

                                                                                                                                    Explanation: Only visible for RealVCE members. You can sign-up / login (it's free).

                                                                                                                                    Question #3

                                                                                                                                    You have the following agent logs.

                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer:

                                                                                                                                    Question #4

                                                                                                                                    You have a repository on github.com that uses the GitHub Copilot coding agent.
                                                                                                                                    You also use the GitHub Copilot CLI locally to reproduce failures and continue the same work from your terminal.
                                                                                                                                    You need to verify the current token usage.
                                                                                                                                    Which Copilot CLI slash command should you run?

                                                                                                                                    • A. /context
                                                                                                                                    • B. /usage
                                                                                                                                    • C. /compact
                                                                                                                                    • D. /diff
                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer: A  🗳️

                                                                                                                                    Explanation: Only visible for RealVCE members. You can sign-up / login (it's free).

                                                                                                                                    Question #5

                                                                                                                                    You have a GitHub repository that uses the GitHub Copilot coding agent.
                                                                                                                                    Developers need the Copilot coding agent to call an internal dependency-scanning API during its run. The API requires an access token.
                                                                                                                                    You need to ensure that the Copilot coding agent can use the token during execution without accessing the repository's Actions secrets and variables. The solution must prevent exposing the token in plaintext.
                                                                                                                                    What should you do?

                                                                                                                                    • A. Add the token as a secret in the Copilot environment.
                                                                                                                                    • B. Store the token in a repository custom instructions file.
                                                                                                                                    • C. Add the token as an Actions repository secret.
                                                                                                                                    • D. Store the token in the agent configuration file.
                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer: A  🗳️

                                                                                                                                    Explanation: Only visible for RealVCE members. You can sign-up / login (it's free).

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