Exam Code: GH-600
Exam Name: Developing in Agentic AI Systems
Certification Provider: Microsoft
Corresponding Certification: GitHub Administrator
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Microsoft GH-600 Exam Syllabus Topics:

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

                                                                                                                                    Microsoft Developing in Agentic AI Systems Sample Questions:

                                                                                                                                    You need to resolve the scoping issue associated to agent1.
                                                                                                                                    What should you do?

                                                                                                                                    • A. Create a fine-grained personal access token (PAT) scoped to product-api and store the PAT as a GitHub Actions secret for agent1 to use.
                                                                                                                                    • B. To the profile of agent1, add a custom instruction specifying that the agent must NOT access billing-service or infra-terraform.
                                                                                                                                    • C. Create a ruleset for billing-service and infra-terraform that blocks push access from the github-actions bot account.
                                                                                                                                    • D. Add a permissions block to the agent1 workflow in product-api.
                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer: A  🗳️

                                                                                                                                    Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

                                                                                                                                    You are running a long GitHub Copilot CLI session. The session has been running for several hours, and you have made significant progress on a multi-file implementation.
                                                                                                                                    Suddenly, you see the Compaction completed message appear without running a command.
                                                                                                                                    What caused the compaction to occur?

                                                                                                                                    • A. The conversation approached 95 percent of the context-window capacity.
                                                                                                                                    • B. The session state file reached the maximum file size.
                                                                                                                                    • C. The session exceeded the maximum allowed duration.
                                                                                                                                    • D. The agent automatically switched to a model that has a larger context window.
                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer: A  🗳️

                                                                                                                                    Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer:

                                                                                                                                    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. MCP server integration
                                                                                                                                    • B. .copilotignore
                                                                                                                                    • C. CODEOWNERS
                                                                                                                                    • D. Tool/terminal invocation permissions in agent mode
                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer: D  🗳️

                                                                                                                                    Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

                                                                                                                                    You have a GitHub repository that uses the GitHub Copilot coding agent.
                                                                                                                                    The agent is assigned to a long-running issue and has already opened a draft pull request linked to the issue. The pull request timeline shows Copilot started work and the pull request description shows periodic status updates.
                                                                                                                                    After 70 minutes, the pull request stops receiving new commits, and the agent session log indicates that the session has timed out. In a pull request comment thread, the agent then proposes changes that no longer match the latest repository guidance.
                                                                                                                                    You need to resume execution in a way that reestablishes the correct context and produces new commits in the existing draft pull request.
                                                                                                                                    What should you do?

                                                                                                                                    • A. Select Approve and run workflows in the pull request merge box.
                                                                                                                                    • B. Close and reopen the issue.
                                                                                                                                    • C. Post a pull request comment that mentions @copilot.
                                                                                                                                    • D. Create a new issue and assign the issue to Copilot.
                                                                                                                                    Reveal Solution  Discussion  0

                                                                                                                                    Correct Answer: C  🗳️

                                                                                                                                    Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).

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