GitHub · Intermediate
GitHub Agentic AI Developer (GH-600)
Operate, integrate, supervise, and govern AI agents in production SDLC workflows — tool use and MCP, memory and state, evaluation and tuning, multi-agent orchestration, and guardrails, with GitHub as the control plane.
GH-600 exam at a glance
| Exam code | GH-600 |
|---|---|
| Vendor | GitHub |
| Level | Intermediate |
| Domains | 6 |
| Published practice questions | 61 |
| Our mock exam | 60 questions in 120 minutes |
| Our practice pass target | 70% |
| Checked against the exam guide | August 10, 2026 |
| Official exam page | GitHub — GH-600 |
The mock exam length, time limit and pass target are our practice settings, not official exam scores or a guarantee of passing. Check the official GH-600 exam page for the current format, price and scoring before you book.
GH-600 exam domains
Weights show our practice allocation, built from the official exam guide; official blueprints may give ranges. Each domain links to its free sample questions (with explanations) and study guide preview once they are published.
Prepare agent architecture and SDLC processes
18%Integrate agents into the software development lifecycle, define boundaries between planning/reasoning/action, and configure observability and control for autonomous agents.
- Identifying steps for agents to perform within the SDLC
- Identifying and mitigating common agent anti-patterns
- Defining inputs, outputs, and success criteria for agents
- Configuring agent planning to be distinct from agent execution
- Configuring an agent to output a structured plan
- Validating agent plans before execution
- Preventing agent action until a plan is checked and approved
- Planning and implementing degrees of agent autonomy, including guardrails
- Configuring agents to produce inspectable artifacts within standard dev tooling
- Configuring human intervention for autonomous agents without slowing delivery
Free Prepare agent architecture and SDLC processes practice questions →Prepare agent architecture and SDLC processes study guide →
Implement tool use and environment interaction
23%Select and configure agent tools, configure MCP servers, integrate agents into development environments, and operate agents with safe execution paths.
- Identifying and configuring required agent tools and tool permissions
- Configuring MCP servers: adding an MCP server as a tool, GitHub remote MCP server
- Configuring MCP registries and MCP allow lists
- Evaluating agent execution context; scoping an agent to a specific repository
- Configuring an agent to be invoked in a CI workflow; branch-based scope
- Enabling autonomous actions: creating branches and pull requests
- Configuring an agent to handle environment-specific constraints
- Implementing error handling, retries, and rollbacks for agent execution
- Implementing escalation paths for failed agent actions
- Implementing traceability and accountability for agent actions
Free Implement tool use and environment interaction practice questions →Implement tool use and environment interaction study guide →
Manage memory, state, and execution
13%Implement agent memory strategies, persist state across sessions, and manage context drift and continuity across tools and environments.
- Choosing between short-term, long-term, and external memory
- Scoping agent memory to task-relevant information
- Defining memory expiration, pruning, and reset rules
- Capturing task progress and decisions as durable artifacts
- Resuming agent work without repeating steps or diverging from prior decisions
- Detecting and correcting context drift during extended agent execution
- Sharing agent state across tools and environments
- Preventing conflicting context between concurrent agent sessions
- Preventing stale context from influencing agent decisions
Free Manage memory, state, and execution practice questions →Manage memory, state, and execution study guide →
Perform evaluation, error analysis, and tuning
18%Define success criteria and evaluation signals for agent tasks, analyze agent failures, and tune agent behavior based on evaluation results.
- Specifying expected outcomes and operational constraints for agent tasks
- Identifying qualitative and quantitative evaluation signals
- Aligning evaluation criteria with development intent
- Generating evaluation signals using automated scanning tools
- Identifying failures using logs, plans, traces, outputs, and workflow artifacts
- Classifying root causes: reasoning errors, tool misuse, context/environment issues
- Revising instructions, workflows, or constraints based on evaluation results
- Refining memory usage and tool usage/access based on evaluation results
Free Perform evaluation, error analysis, and tuning practice questions →Perform evaluation, error analysis, and tuning study guide →
Orchestrate multi-agent coordination
18%Operate and manage multi-agent workflows, configure observability for multi-agent behavior, and detect/respond to multi-agent failures.
- Applying orchestration patterns to coordinate multiple agents
- Configuring agent isolation for parallel execution
- Detecting and resolving agent conflicts: overlapping changes, duplicated effort, contradictory outputs
- Configuring multi-agent workflows to produce artifacts suitable for review and audit
- Documenting key decisions, handoffs, and outcomes across agents
- Performing post-hoc analysis of multi-agent behavior
- Identifying failed, partial, or stalled agent executions
- Implementing multi-agent recovery patterns, including rollback and human-in-the-loop
- Adding, updating, reconfiguring, or replacing agents without disrupting active workflows
- Retiring agents while preserving auditability and workflow continuity
Free Orchestrate multi-agent coordination practice questions →Orchestrate multi-agent coordination study guide →
Implement guardrails and accountability
10%Define autonomy levels and implement guardrails and human-in-the-loop workflows to balance delivery velocity with security, compliance, and Responsible AI standards.
- Classifying agent actions by operational, security, and compliance risk
- Assigning autonomy levels to maximize delivery speed while remaining compliant
- Identifying the subset of actions that require human judgment
- Blocking actions that violate security, compliance, or Responsible AI policies
- Scoping permissions and execution contexts for least-privilege access
- Requiring explicit authorization for irreversible or compliance-sensitive changes
- Preserving execution velocity by minimizing approvals that don't materially reduce risk
Free Implement guardrails and accountability practice questions →Implement guardrails and accountability study guide →
GH-600 FAQ
How is the GitHub Agentic AI Developer (GH-600) practice assessment configured?
Our practice format targets 60 questions in 120 minutes. Availability depends on the published question bank and is shown on this course page. Official exam question counts and formats may differ.
What does the 70% practice target mean?
Check the provider's current exam guide for official scoring. A practice percentage is not an official scaled score. DataCertPrep uses 70% as an internal practice target, not a prediction or guarantee of passing.
How should I assess my progress in GitHub Agentic AI Developer (GH-600)?
Use practice accuracy to find weak topics. Repeating familiar questions can inflate your score. Check coverage of the current objectives, practise hands-on tasks, and assess yourself on unseen questions.
What is the best way to study GitHub Agentic AI Developer (GH-600)?
Read the available guides, work through practice explanations, use flashcards and complete practical exercises. Take a timed assessment when available. Check the provider's current exam guide before booking your exam.
Should I use GH-600 exam dumps?
No. "Dumps" are questions copied or recalled from the live exam. Certification exams are covered by a candidate agreement that prohibits sharing or using them, and breaking it can lead to a revoked certification or a ban from future exams. Dumps are also often wrong, and they go out of date when the exam blueprint changes. Memorising answers does not build the skills the exam tests. Study the official GitHub exam guide and practise with original questions that explain each answer, such as the free sample questions linked on this page.