Exam Code: GH-600
Exam Name: GitHub Agentic AI Developer
Certification Provider: Microsoft
Corresponding Certification: GitHub Administrator

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Microsoft GH-600 Exam Syllabus Topics:

Section	Weight	Objectives
Implement tool use and environment interaction	20–25%	- Development environment integration 1. Scope agents to repositories or branches 2. Enable CI-based agent execution 3. Enable autonomous actions (PRs, branches) - MCP server configuration 1. Configure registries and allow lists 2. Add MCP servers to agents - Agent tool configuration 1. Select and configure tools 2. Configure tool permissions and scope - Safe execution and error handling 1. Retries and rollback strategies 2. Escalation paths and traceability
Orchestrate multi-agent coordination	15–20%	- Lifecycle management 1. Add/replace/retire agents safely - Observability and auditability 1. Generate logs and artifacts for review 2. Document agent handoffs and decisions - Multi-agent workflows 1. Resolve conflicts and overlaps 2. Coordinate parallel agent execution - Failure handling and recovery 1. Implement rollback and recovery patterns 2. Detect stalled or degraded agents
Evaluation, error analysis, and tuning	15–20%	- Define evaluation criteria 1. Generate automated evaluation signals 2. Define success metrics and constraints - Tuning agent behavior 1. Optimize memory usage and constraints 2. Refine prompts, tools, and workflows - Failure analysis 1. Analyze logs, traces, and artifacts 2. Classify reasoning, tool, and context errors
Manage memory, state, and execution	10–15%	- Cross-tool continuity 1. Share state across tools and environments 2. Prevent stale or conflicting context - State persistence and drift control 1. Detect and correct context drift 2. Persist task progress as artifacts - Agent memory strategies 1. Short-term vs long-term memory selection 2. Memory scoping and expiration rules
Prepare agent architecture and SDLC processes	15–20%	- Observability and control 1. Define autonomy levels and guardrails 2. Produce inspectable artifacts in GitHub 3. Enable human-in-the-loop controls - Integrate agents into SDLC workflows 1. Define inputs, outputs, and success criteria 2. Define agent steps in SDLC 3. Identify and mitigate agent anti-patterns - Planning vs execution boundaries 1. Validate structured agent plans 2. Prevent execution before approval 3. Separate planning and execution phases
Implement guardrails and accountability	10–15%	- Autonomy and risk levels 1. Assign autonomy levels with compliance constraints 2. Classify agent actions by risk - Guardrails and human-in-the-loop 1. Enforce least-privilege execution 2. Require approvals for sensitive actions

Microsoft GitHub Agentic AI Developer Sample Questions:

Question #1

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 list and specify the --status and --jq parameters.
B. Run gh agent-task view and specify the --log and --jq parameters.
C. Run gh agent-task view and specify the --log and --follow parameters.
D. Run gh agent-task list and specify the --web parameter.

Answer: C