Microsoft GH-600 : Developing in Agentic AI Systems

  • Exam Code: GH-600
  • Exam Name: Developing in Agentic AI Systems
  • Updated: Nov 03, 2026
  • Q & A: 110 Questions and Answers

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About Microsoft GH-600 Exam Questions

An internationally recognized Microsoft certification is a capacity standard employers understand in any country. BraindumpsPrep lays the groundwork for passing Microsoft Developing in Agentic AI Systems with 110 verified GH-600 practice questions.

Microsoft GH-600 Exam Overview:

Certification Vendor:Microsoft / GitHub
Exam Name:Developing in Agentic AI Systems
Exam Number:GH-600
Exam Format:Interactive items, Proctored exam, Multiple-choice questions, Scenario-based questions
Real Exam Qty:Approximately 40–60
Certificate Validity Period:1 year
Exam Duration:120 minutes
Related Certifications:GitHub Certified: Agentic AI Developer
Exam Price:$165 USD
Passing Score:700/1000
Available Languages:English
Sample Questions:Free Download real GH-600 actual tests
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
Topic 1: Implement guardrails and accountability10–15%- Implement guardrails and human-in-the-loop workflows
  • 1. Scope permissions and execution contexts to enforce least-privilege access
    • 2. Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes
      • 3. Block actions that violate defined security, compliance, or Responsible AI policies
        • 4. Identify the subset of actions that require human judgment
          • 5. Preserve execution velocity by minimizing approvals that do not materially reduce risk
            - 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
                Topic 2: Manage memory, state, and execution10–15%- Persist agent state and manage context drift
                • 1. Detect and correct drift during extended agent execution
                  • 2. Resume agent work without repeating steps or diverging from prior decisions
                    • 3. Capture task progress and decisions as durable artifacts
                      - Ensure continuity of agent memory and state across tools and environments
                      • 1. Share agent state
                        • 2. Prevent stale context
                          • 3. Prevent conflicting context
                            - Implement agent memory strategies
                            • 1. Choose between short-term, long-term, and external memory
                              • 2. Scope agent memory to task-relevant information
                                • 3. Define memory expiration, pruning, and reset rules
                                  Topic 3: Perform evaluation, error analysis, and tuning15–20%- 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. Refine memory usage
                                        • 2. Refine tool usage and tool access
                                          • 3. Revise instructions, workflows, or constraints
                                            - Define success criteria and evaluation signals for agent tasks
                                            • 1. Generate evaluation signals by using automated scanning tools
                                              • 2. Specify expected outcomes and operational constraints for agent tasks
                                                • 3. Align evaluation criteria with development intent
                                                  • 4. Identify qualitative and quantitative evaluation signals to evaluate agents
                                                    Topic 4: 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. Document key decisions, handoffs, and outcomes across agents
                                                        • 3. Perform post-hoc analysis of multi-agent behavior
                                                          - 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
                                                                - Detect and respond to multi-agent failures and degraded behavior
                                                                • 1. Identify failed, partial, or stalled agent executions
                                                                  • 2. Respond to degraded behavior or coordination across agents
                                                                    • 3. Implement multi-agent recovery patterns, including rollback and human-in-the-loop
                                                                      - Operate and manage multi-agent workflows
                                                                      • 1. Configure agent isolation for parallel execution
                                                                        • 2. Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs
                                                                          • 3. Apply an orchestration pattern to coordinate multiple agents
                                                                            Topic 5: Prepare agent architecture and SDLC processes15–20%- Configure observability and control for autonomous agents
                                                                            • 1. Configure human intervention for autonomous agents without slowing delivery
                                                                              • 2. Plan and implement the degree of agent autonomy, including guardrails
                                                                                • 3. Configure agents to produce inspectable artifacts within standard development tooling
                                                                                  - Integrate agents into the software development lifecycle (SDLC)
                                                                                  • 1. Define inputs, outputs, and success criteria for agents
                                                                                    • 2. Identify and mitigate common anti-patterns in agents
                                                                                      • 3. Identify steps for agents to perform
                                                                                        - Define boundaries between planning, reasoning, and action
                                                                                        • 1. Validate agent plans
                                                                                          • 2. Configure an agent to output a structured plan
                                                                                            • 3. Prevent agent action until the agent checks and approves
                                                                                              • 4. Configure agent planning to be distinct from agent execution
                                                                                                Topic 6: Implement tool use and environment interaction20–25%- Configure MCP servers
                                                                                                • 1. Configure MCP allow lists
                                                                                                  • 2. Configure MCP registries
                                                                                                    • 3. Configure a GitHub remote MCP server
                                                                                                      • 4. Add an MCP server as a tool to an agent
                                                                                                        - Select and configure agent tools
                                                                                                        • 1. Configure agent tools
                                                                                                          • 2. Configure agent tool permissions
                                                                                                            • 3. Identify required tools
                                                                                                              - Integrate agents within development environments
                                                                                                              • 1. Enable an agent to perform autonomous actions, including creating branches and pull requests
                                                                                                                • 2. Evaluate the execution context for an agent
                                                                                                                  • 3. Configure an agent to handle environment-specific constraints
                                                                                                                    • 4. Configure an agent to use branch-based scope
                                                                                                                      • 5. Configure an agent to be invoked in a CI workflow
                                                                                                                        • 6. Configure an agent's scope to a specific repository
                                                                                                                          - Operate agents with safe execution paths and robust error handling
                                                                                                                          • 1. Implement error handling
                                                                                                                            • 2. Implement rollbacks
                                                                                                                              • 3. Implement retries
                                                                                                                                • 4. Implement escalation paths
                                                                                                                                  • 5. Implement traceability and accountability for agent actions

                                                                                                                                    Microsoft Developing in Agentic AI Systems: The Questions Everyone Asks First

                                                                                                                                    Microsoft Developing in Agentic AI Systems is an official Microsoft / GitHub exam, registered under exam code GH-600. Passing it earns the GitHub Administrator certification at the Intermediate level. It also links to GitHub Certified: Agentic AI Developer. As an internationally recognized capacity standard, this credential speaks for your ability wherever your career takes you.

                                                                                                                                    The Microsoft Developing in Agentic AI Systems syllabus comprises 6 official domains. The heaviest include Orchestrate multi-agent coordination (15–20%), Manage memory, state, and execution (10–15%), and Prepare agent architecture and SDLC processes (15–20%). The complete outline is above on this page; a short, efficient study plan starts with knowing exactly where the marks live.

                                                                                                                                    The Microsoft Developing in Agentic AI Systems exam packs Approximately 40–60 questions into 120 minutes. Short-term preparation works best when every session is realistic, so run timed simulations in the BraindumpsPrep engine, keep your flag-and-return rhythm tight, and let repeated full-length runs build the pace the clock demands.

                                                                                                                                    Microsoft Developing in Agentic AI Systems requires 700/1000 to pass, with an official registration fee of $165 USD. Retakes bill the full $165 USD again, so speed should never mean gambling. Compress your preparation with the BraindumpsPrep practice tests, and book once your scores clear the requirement reliably.

                                                                                                                                    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.

                                                                                                                                    Policies change, so verify the current requirements before registering on the official exam page.

                                                                                                                                    Yes to both. BraindumpsPrep provides a free demo of the Microsoft Developing in Agentic AI Systems questions so you can verify the quality first, and after purchase the newest practice material is free for one year from the date of your order. When that year ends, extending the update service costs 50% of the regular price.

                                                                                                                                    Your purchase carries a 100% money-back guarantee with defined conditions. Take the Microsoft Developing in Agentic AI Systems exam within 60 days of purchase; if you fail, you may claim a full refund, provided the exam matches your product. Attempts within 3 days of purchase are ineligible, as are downloaded-but-unused products, free materials, and expired orders; the candidate name must match the payer name. Submit a scanned enrollment slip and the official Score Report PDF within 2 days of the exam, and claims are processed within 7 days. Alternatively, exchange for two other exam products of equal value, free, with the update service on your original purchase retained.

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                                                                                                                                    Microsoft Developing in Agentic AI Systems Sample Questions:

                                                                                                                                    Question #1

                                                                                                                                    You have a GitHub repository that runs an agentic software development lifecycle workflow by using GitHub Actions. The workflow uses the following three executors implemented as scripts: spec_analyzer, risk_reviewer, and plan_merger.
                                                                                                                                    You need to coordinate multiple specialized agents so that analysis and risk review run in parallel and then a final executor merges the outputs into a single plan. The orchestration pattern must fan out one request to multiple executors and then fan in the results to a final executor.
                                                                                                                                    How should you complete the workflow definition? To answer, drag the appropriate values to the correct targets.

                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer:


                                                                                                                                    Explanation:
                                                                                                                                    Target 1 # E. needs
                                                                                                                                    Target 2 # A. [spec_analyzer, risk_reviewer]
                                                                                                                                    Target 3 # B. concurrency
                                                                                                                                    The relevant completed section is:
                                                                                                                                    plan_merger:
                                                                                                                                    runs-on: ubuntu-latest
                                                                                                                                    needs: [spec_analyzer, risk_reviewer]
                                                                                                                                    steps:
                                                                                                                                    - name: Merge executor outputs into a single plan
                                                                                                                                    run: ./executors/plan_merger.sh
                                                                                                                                    - name: Publish merged plan
                                                                                                                                    run: echo " publish plan artifact "
                                                                                                                                    concurrency:
                                                                                                                                    group: multiagent-${{ github.ref }}
                                                                                                                                    The plan_merger job must use needs: [spec_analyzer, risk_reviewer] . GitHub Actions jobs run in parallel by default when no dependency exists. Therefore, spec_analyzer and risk_reviewer can execute concurrently, implementing the fan-out phase. The needs array then establishes the fan-in dependency: plan_merger does not start until both upstream jobs complete successfully. GitHub explicitly defines jobs. < job_id > .needs as the mechanism for declaring jobs that must complete before another job runs.
                                                                                                                                    The final blank is concurrency , because the following line already defines group: multiagent-${{ github.ref
                                                                                                                                    }}. GitHub ' s concurrency key groups workflow jobs or runs under a shared key so that overlapping executions for the same reference can be controlled. This is separate from the parallelism of the two specialized analyzer jobs.
                                                                                                                                    This architecture directly implements a multi-agent orchestration pattern: independent specialized executors operate concurrently, their outputs converge at a deterministic coordination point, and execution boundaries are managed explicitly. The GH-600 study guide specifically requires candidates to apply orchestration patterns, configure parallel agent execution, and detect or prevent conflicts among concurrently operating agents.
                                                                                                                                    Study Guide Reference Topics: Orchestrate Multi-Agent Coordination; fan-out/fan-in orchestration; parallel execution; dependency management; agent isolation and conflict control.

                                                                                                                                    Question #2

                                                                                                                                    You have a GitHub repository that uses the GitHub Copilot CLI to run autonomous tasks.
                                                                                                                                    You need to validate each generated command before it runs. Any commands that attempt to modify paths outside the repository must be blocked.
                                                                                                                                    How should you complete the YAML? To answer, drag the appropriate values to the correct targets. Each value 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.

                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer:


                                                                                                                                    Explanation:
                                                                                                                                    Target
                                                                                                                                    Intended selection in the supplied question
                                                                                                                                    event
                                                                                                                                    pre-exec
                                                                                                                                    cli.hooks.config
                                                                                                                                    copilot/hooks.yaml
                                                                                                                                    Verification correction: These are the intended selections within the supplied YAML. Current official Copilot documentation specifies JSON hook configuration and the preToolUse event; the supplied YAML is not a verified current Copilot CLI configuration.
                                                                                                                                    Within the question's own schema, pre-exec is the logical event because validation must occur before a command can modify the filesystem. A post-exec event would run after the potentially prohibited action. The configuration path must reference the hook file identified in the exhibit, .copilot/hooks.yaml, rather than a workflow file under .github/workflows.
                                                                                                                                    However, official Copilot documentation defines repository hook files under .github/hooks/*.json and uses the preToolUse lifecycle event. A blocking hook can return a structured permission decision of " deny " . The supplied YAML names and configuration structure therefore should not be copied into a current deployment as documented syntax.
                                                                                                                                    The depicted command-string filter also does not establish complete filesystem confinement. Detecting obvious absolute paths and parent traversal cannot reliably resolve symbolic links, variable expansion, or the behavior of invoked programs. Enforcing the requirement that all external modifications be blocked requires a genuine execution boundary alongside command validation.
                                                                                                                                    The question tests the correct preventive-control concept but presents an implementation that needs correction. Relevant curriculum topics are blocking prohibited actions, enforcing least privilege, and constraining execution contexts.
                                                                                                                                    Reference: GitHub Copilot hooks reference .

                                                                                                                                    Question #3

                                                                                                                                    You have a GitHub Copilot coding agent named CodeAgent. The .agent.md file of CodeAgent contains the following YAML frontmatter.
                                                                                                                                    name: CodeAgent
                                                                                                                                    description: Performs repository analysis and code review tasks.
                                                                                                                                    tools: [ ' edit ' , ' execute ' , ' read ' , ' search ' ]
                                                                                                                                    You need to issue a GitHub Copilot CLI command that preserves execution velocity for read-only tasks by eliminating approval prompts for low-risk tools. The solution must ensure that high-risk tools that can make changes remain available but still require explicit user approval before running.
                                                                                                                                    Which command should you run?

                                                                                                                                    • A. copilot agent run CodeAgent --allow-all-tools
                                                                                                                                    • B. copilot agent run CodeAgent --deny-tool ' edit,execute '
                                                                                                                                    • C. copilot agent run CodeAgent --allow-tool ' read,search '
                                                                                                                                    • D. copilot agent run CodeAgent
                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer: C  🗳️

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                                                                                                                                    Question #4

                                                                                                                                    You need to make changes to repo1 to support the planned changes for the agents.
                                                                                                                                    What should you modify?

                                                                                                                                    • A. .github/agents/*.agent.md
                                                                                                                                    • B. < project > /.mcp/server.json
                                                                                                                                    • C. .vscode/mcp.json
                                                                                                                                    • D. .vscode/settings.json
                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer: A  🗳️

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

                                                                                                                                    Question #5

                                                                                                                                    You have a GitHub repository that contains an agent named Orchestrator. Orchestrator delegates work to the following specialized subagents:
                                                                                                                                    * Planner reviews issues and creates a plan of action.
                                                                                                                                    * Implementer writes code based on the plan of action.
                                                                                                                                    * Reviewer reviews the code.
                                                                                                                                    You create a new agent named Summarizer that produces a concise summary of the work performed by the other agents.
                                                                                                                                    You need to ensure that Orchestrator can invoke Summarizer as part of its workflow.
                                                                                                                                    What should you do?

                                                                                                                                    • A. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the tools list.
                                                                                                                                    • B. In the YAML frontmatter of the Orchestrator agent, add Summarizer to the agents list.
                                                                                                                                    • C. In the YAML frontmatter of the Reviewer agent, add Summarizer to the agents list.
                                                                                                                                    • D. In the YAML frontmatter of the Reviewer agent, add a handoffs entry that points to the Summarizer agent.
                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer: B  🗳️

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

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