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Updated: Sep 18, 2026

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Microsoft GH-600 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Developing in Agentic AI Systems
Exam Number:GH-600
Exam Price:USD 100
Real Exam Qty:40–60
Certificate Validity Period:2 years
Passing Score:700
Exam Duration:120–150
Exam Format:Performance-based/lab tasks, Scenario-based, Multiple choice, Multiple select
Related Certifications:Microsoft Certified: Azure AI Engineer Associate
Microsoft Certified: Azure Developer Associate
Available Languages:English
Recommended Training:GH-600 Learning Paths on Microsoft Learn
Semantic Kernel Documentation
Azure AI Foundry Documentation
Exam Registration:Pearson VUE Registration
Microsoft GH-600 Exam Registration
Sample Questions:Microsoft GH-600 Sample Questions
Exam Way:Online proctored or Pearson VUE test center
Pre Condition:Recommended: AZ-204 (Azure Developer Associate) or AI-102 (Azure AI Engineer Associate); proficiency in Python or C# and Azure development experience
Official Syllabus URL:https://learn.microsoft.com/en-us/certifications/exams/gh-600

Microsoft GH-600 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Implement agents and multi-agent systems30%- Build agents with Azure AI tools and frameworks
  • 1. Implement agent logic and reasoning
    • 2. Develop using Semantic Kernel and Azure AI Foundry
      • 3. Integrate models and prompts
        - Orchestrate multi-agent collaboration
        • 1. Define communication protocols between agents
          • 2. Manage agent handoffs and task distribution
            • 3. Implement workflows and coordination strategies
              Topic 2: Design agentic AI solutions25%- Define requirements for agentic systems
              • 1. Identify use cases and scenarios
                • 2. Plan for responsible AI and governance
                  • 3. Define functional and non-functional requirements
                    - Design agent architecture
                    • 1. Plan tool integration and orchestration
                      • 2. Select agent patterns and topologies
                        • 3. Design memory and state management
                          Topic 3: Integrate tools, data, and services25%- Incorporate external tools and APIs
                          • 1. Design and register tool definitions
                            • 2. Handle authentication and error resilience
                              • 3. Implement function calling and service integration
                                - Connect data sources and knowledge bases
                                • 1. Implement retrieval-augmented generation (RAG)
                                  • 2. Ensure data security and access control
                                    • 3. Integrate vector databases and search
                                      Topic 4: Test, deploy, and monitor agentic AI systems20%- Validate agent performance and safety
                                      • 1. Evaluate quality metrics and iterate
                                        • 2. Test reasoning accuracy and consistency
                                          • 3. Apply guardrails and content safety
                                            - Deploy and monitor agents at scale
                                            • 1. Deploy to Azure AI and cloud environments
                                              • 2. Optimize cost, latency, and throughput
                                                • 3. Implement logging, telemetry, and observability

                                                  Microsoft Developing in Agentic AI Systems Sample Questions:

                                                  Question #1

                                                  You are architecting an agentic AI system and need the agent's tool-calling behavior to be constrained so it can only call a specific allow-listed set of MCP tools, never arbitrary ones. What should you configure?

                                                  • A. /usage
                                                  • B. Repository ruleset
                                                  • C. Tool/server allow-list in the MCP client configuration
                                                  • D. .copilotignore
                                                  Reveal Solution  Discussion  0

                                                  Correct Answer: C  🗳️

                                                  Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).

                                                  Question #2

                                                  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_log audit log event
                                                  Reveal Solution  Discussion  0

                                                  Correct Answer: A  🗳️

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                                                  Question #3

                                                  You have a GitHub Actions workflow that runs two jobs named agent-plan and agent-implement on pull requests.
                                                  The agent-plan job generates an implementation plan that downstream jobs must consume, but the job currently writes the plan only to the console.
                                                  You need to capture the implementation plan as a step output and configure the workflow so that downstream jobs can reference the output.
                                                  How should you configure the workflow? To answer, select the appropriate options in the answer area.
                                                  NOTE: Each correct selection is worth one point.

                                                  Reveal Solution  Discussion  0

                                                  Correct Answer:

                                                  Question #4

                                                  You have a GitHub repository that uses GitHub Actions to validate pull requests opened by the GitHub Copilot coding agent. The workflow runs unit tests and a linter on pull request triggers, and Copilot opens draft pull requests on dedicated branches while iterating by using commits.
                                                  You discover that when multiple Copilot sessions push updates to the same pull request branch in quick succession, multiple workflow runs execute concurrently.
                                                  You need to enable parallel workflow executions across different pull request branches.
                                                  How should you configure workflow-level concurrency? To answer, select the appropriate options in the answer area.
                                                  NOTE: Each correct selection is worth one point.

                                                  Reveal Solution  Discussion  0

                                                  Correct Answer:

                                                  Question #5

                                                  You have multiple GitHub Copilot coding agents that run tasks concurrently.
                                                  You are monitoring the agents from the terminal by using the GitHub CLI.
                                                  An agent appears stalled.
                                                  You need to live stream the session log output.
                                                  What should you do?

                                                  • A. Run gh agent-task view and specify the --log and --jq parameters.
                                                  • B. Run gh agent-task view and specify the --log and --follow parameters.
                                                  • C. Run gh agent-task list and specify the --web parameter.
                                                  • D. Run gh agent-task list and specify the --status and --jq parameters.
                                                  Reveal Solution  Discussion  0

                                                  Correct Answer: B  🗳️

                                                  Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).

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