Free Practice Questions for GitHub AI Agent Operations Professional (GH-600) Certification
Study with 344 exam-style practice questions designed to help you prepare for the GitHub AI Agent Operations Professional (GH-600).
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Exam Details
Key information about GitHub AI Agent Operations Professional (GH-600)
- Multiple choice
- Matching
- Fill in the blank
Experience with the software development lifecycle (SDLC), workflows in GitHub and controls, code quality, security, and review practices. Experience with coding agents including GitHub Copilot, MCP servers, and agent customization such as custom instructions, custom agents, tools, and Copilot setup steps.
Subject matter experts in operating, integrating, supervising, and governing AI agents inside production-grade SDLC workflows and development environments.
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
Domain 1: Prepare agent architecture and SDLC processes
Subdomain 1.1: Integrate agents into the software development lifecycle (SDLC)
Identify steps for agents to perform Identify and mitigate common anti-patterns in agents Define inputs, outputs, and success criteria for agents
Subdomain 1.2: Define boundaries between planning, reasoning, and action
Configure agent planning to be distinct from agent execution Configure an agent to output a structured plan Validate agent plans Prevent agent action until the agent checked and approved
Subdomain 1.3: Configure observability and control for autonomous agents
Plan and implement the degree of agent autonomy, including guardrails Configure agent to produce inspectable artifacts within standard development tooling Configure human intervention for autonomous agents without slowing delivery
Domain 2: Implement tool use and environment interaction
Subdomain 2.1: Select and configure agent tools
Identify required tools Configure agent tools Configure agent tool permissions
Subdomain 2.2: Configure MCP servers
Add an MCP server as a tool to an agent Configure a GitHub remote MCP server Configure the MCP registries Configure MCP allow lists
Subdomain 2.3: Integrate agents within development environments
Evaluate the execution context for an agent Configure an agent's scope to a specific repository Configure an agent to be invoked in a CI workflow Configure an agent to use branch-based scope Enable an agent to perform autonomous actions, including creating branches and pull requests Configure an agent to handle environment-specific constraints
Subdomain 2.4: Operate agents with safe execution paths and robust error handling
Implement error handling Implement retries Implement rollbacks Implement escalation paths Implement traceability and accountability for agent actions
Domain 3: Manage memory, state, and execution
Subdomain 3.1: Implement agent memory strategies
Choose between short-term, long-term, and external memory Scope agent memory to task-relevant information Define memory expiration, pruning, and reset rules
Subdomain 3.2: Persist agent state and manage context drift
Capture task progress and decisions as durable artifacts Resume agent work without repeating steps or diverging from prior decisions Detect and correct drift during extended agent execution
Subdomain 3.3: Ensure continuity of agent memory and state across tools and environments
Share agent state Prevent conflicting context Prevent stale context
Domain 4: Perform evaluation, error analysis, and tuning
Subdomain 4.1: Define success criteria and evaluation signals for agent tasks
Specify expected outcomes and operational constraints for agent tasks Identify qualitative and quantitative evaluation signals to evaluate agents Align evaluation criteria with development intent Generate evaluation signals by using automated scanning tools
Subdomain 4.2: Analyze agent failures and identify root causes
Identify failures by using logs, plans, traces, outputs, and workflow artifacts Classify root causes, including reasoning errors, tool misuse, and context or environment issues
Subdomain 4.3: Tune agent behavior based on evaluation results
Revise instructions, workflows, or constraints Refine memory usage Refine tool usage and tool access
Domain 5: Orchestrate multi-agent coordination
Subdomain 5.1: Operate and manage multi-agent workflows
Apply an orchestration pattern to coordinate multiple agents Configure agent isolation for parallel execution Detect and resolve agent conflicts, including overlapping code changes, duplicated effort, and contradictory outputs
Subdomain 5.2: Configure observability for multi-agent behavior by using logs, artifacts, and operational signals
Configure multi-agent workflows to produce artifacts suitable for review and audit Document key decisions, handoffs, and outcomes across agents Perform post-hoc analysis of multi-agent behavior
Subdomain 5.3: Detect and respond to multi-agent failures and degraded behavior
Identify failed, partial, or stalled agent executions Respond to degraded behavior or coordination across agents Implement multi-agent recovery patterns, including rollback and human-in-the-loop
Subdomain 5.4: Manage the lifecycle of agents within multi-agent workflows
Add agents to existing multi-agent workflows Update, reconfigure, or replace agents without disrupting active workflows Retire agents while preserving auditability and workflow continuity
Domain 6: Implement guardrails and accountability
Subdomain 6.1: Define autonomy levels
Classify agent actions by operational, security, and compliance risk to right-size human interventions Assign autonomy levels to maximize delivery speed while remaining compliant with organizational security and Responsible AI standards
Subdomain 6.2: Implement guardrails and human-in-the-loop workflows
Identify the subset of actions that require human judgment Block actions that violate defined security, compliance, or Responsible AI policies Scope permissions and execution contexts to enforce least-privilege access Require explicit authorization or controlled paths for irreversible or compliance-sensitive changes Preserve execution velocity by minimizing approvals that do not materially reduce risk
Techniques & products