Career upgrade: Learn practical AI skills for better jobs and higher pay.
Level up
PracticeVideosBlogFlashcardsEspañol
All Practice Exams

100+ Free GitHub Agentic AI Developer Practice Questions

Pass your GitHub Certified: Agentic AI Developer (Exam GH-600) exam on the first try — instant access, no signup required.

✓ No registration✓ No credit card✓ No hidden fees✓ Start practicing immediately
100+ Questions
100% Free
1 / 100
Question 1
Score: 0/0

An organization wants to ensure a GitHub Copilot agent cannot push directly to the main branch under any circumstances. Which GitHub platform control best enforces this?

A
B
C
D
to track
Same family resources

Explore More GitHub Certifications

Continue into nearby exams from the same family. Each card keeps practice questions, study guides, flashcards, videos, and articles in one place.

GitHub Actions Certification

Practice Questions1 blog

GitHub Foundations

Practice Questions1 video1 blog

GitHub Administration (GH-100)

Practice Questions

GitHub Advanced Security (GHAS) Certification

Practice Questions

GitHub Copilot Certification

Practice Questions

More From This Family

Videos and articles for deeper review.

VideoGitHub Foundations Exam Guide 2026: GH-900 Prep That Uses GitHubArticleGitHub Actions Certification Guide 2026: GH-200 Prep10 min readArticleGitHub Foundations Exam Guide 2026: GH-900 Prep That Uses GitHub4 min read
2026 Statistics

Key Facts: GitHub Agentic AI Developer Exam

700/1000

Passing Score

Microsoft Learn GH-600 study guide

~40–60

Exam Questions

Microsoft Learn (scenario-based)

120 min

Exam Time

Microsoft Learn

6

Exam Domains

GH-600 study guide

July 2026

GA Release

Microsoft Community Hub

Annual

Renewal

Free online assessment via Microsoft Learn

GH-600 requires a scaled score of 700 out of 1000 with approximately 40–60 scenario-based questions in 120 minutes. The credential is a new 2026 role-based certification (beta through May 31, 2026; GA July 2026). Preparation typically takes 4–8 weeks and focuses on GitHub Copilot agent mode, MCP server configuration, memory and state management, multi-agent orchestration, and Responsible AI guardrails.

Sample GitHub Agentic AI Developer Practice Questions

Try these sample questions to test your GitHub Agentic AI Developer exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1In the GH-600 exam domain 'Prepare agent architecture and SDLC processes,' what is the primary purpose of defining clear boundaries between an agent's planning phase and its execution phase?
A.To ensure the agent uses fewer API tokens during code generation
B.To allow human review of the agent's structured plan before any irreversible actions are taken
C.To enable the agent to run multiple tasks simultaneously in parallel
D.To reduce the number of MCP servers required for a workflow
Explanation: Separating planning from execution allows operators to validate the agent's intended actions before they are carried out, which is a core safety principle for agentic AI systems. This human-in-the-loop checkpoint prevents unintended side effects from autonomous actions. The other options describe unrelated concerns such as token efficiency, parallelism, and MCP topology.
2A developer wants to add a third-party data-retrieval service as a tool available to a GitHub Copilot agent. Which GitHub feature is the correct mechanism for exposing that external service to the agent?
A.GitHub Actions secrets
B.GitHub Codespaces devcontainer.json
C.Model Context Protocol (MCP) server
D.GitHub Packages registry
Explanation: Model Context Protocol (MCP) servers are the standard GitHub Copilot mechanism for exposing external tools, APIs, and data sources to an agent. Configuring an MCP server allows the agent to call the external service with defined permissions and allow-lists. Secrets manage credentials, devcontainer.json configures development environments, and Packages manages software artifacts.
3When configuring MCP servers for a GitHub Copilot agent, what is the purpose of an MCP allow list?
A.It lists the GitHub repositories the agent is permitted to clone
B.It specifies which MCP servers and tools the agent is authorized to invoke
C.It enumerates the GitHub Actions workflows the agent can trigger
D.It defines the set of pull request reviewers the agent may assign
Explanation: An MCP allow list restricts the agent to only the MCP servers and specific tools that an administrator has explicitly approved, enforcing least-privilege access. Without an allow list, an agent could theoretically call any registered MCP tool, creating a broader attack surface. The other options describe repository access, workflow triggers, and PR reviewer assignment, which are separate access-control concerns.
4You need a GitHub Copilot agent to automatically create a feature branch and open a pull request when a task is complete. Which configuration must be explicitly enabled to allow this autonomous action?
A.The agent must be given a personal access token with admin:org scope
B.The agent must be configured with permissions to perform autonomous actions, including creating branches and pull requests
C.The repository must be made public so the agent can write to it
D.A GitHub Actions workflow must manually approve every branch the agent creates
Explanation: The GH-600 study guide specifically lists 'Enable an agent to perform autonomous actions, including creating branches and pull requests' as a distinct configuration step under tool use and environment interaction. This explicit permission grant is necessary before the agent can take such actions. Making a repository public or requiring manual approval for every branch contradicts the goal of autonomous operation.
5A GitHub Copilot agent is configured to run inside a CI workflow. Which benefit does invoking the agent within CI provide that running it manually in the IDE does not?
A.The agent gains access to GitHub Models endpoints that are unavailable outside CI
B.The agent can use a larger context window than is available in the Copilot Chat IDE panel
C.Agent execution is triggered automatically on code events, enabling consistent and auditable automation without human initiation
D.CI execution bypasses the MCP allow list restrictions configured for the repository
Explanation: Integrating an agent into a CI workflow means it can be triggered by GitHub events such as push, pull_request, or schedule, producing consistent, auditable, and repeatable automation without requiring a human to manually invoke it each time. The other options describe inaccurate capabilities or explicitly violate security principles covered on the exam.
6What is 'context drift' in the context of long-running GitHub Copilot agents?
A.The gradual degradation of code quality when an agent rewrites files over multiple iterations
B.A security vulnerability where the agent's context window leaks information to external MCP servers
C.A situation where the agent's working assumptions diverge from the current repository state during extended execution
D.The accidental inclusion of irrelevant files in the agent's memory due to misconfigured repository scoping
Explanation: Context drift occurs when an agent's internal representation of the task or codebase falls out of sync with reality during a long-running execution, for example because other developers made commits or because earlier agent decisions are no longer consistent with new information. The GH-600 study guide lists detecting and correcting drift as a key memory and state management skill. The other options describe code quality, security, or scoping issues, not context drift specifically.
7When should a GitHub Copilot agent use long-term memory instead of short-term (in-context) memory for storing a completed task's results?
A.When the task result is less than 100 tokens and can be summarized easily
B.When the agent needs to reuse information across multiple separate task sessions or after context resets
C.When the model being used does not support function calling
D.When the repository has more than 10,000 files and context is too large to hold
Explanation: Long-term memory persists information beyond a single context window or session, allowing agents to resume work or reuse prior decisions in future executions. The GH-600 study guide identifies choosing between short-term and long-term memory as a key skill under memory management. The other options describe token limits, model capabilities, or repository size, which are not the primary driver for choosing long-term memory.
8A developer is configuring a GitHub Copilot agent to scope its execution to a specific repository. What is the primary security benefit of this scoping?
A.It prevents the agent from consuming GitHub Actions minutes outside the specified repository
B.It limits the agent's read/write access to only the intended codebase, reducing the blast radius of errors or misuse
C.It forces the agent to use only models available through GitHub Models for that repository
D.It automatically enables required reviewers on all pull requests the agent opens
Explanation: Scoping an agent to a specific repository enforces least-privilege access: the agent can only interact with that repository's code, issues, and branches, limiting the potential damage from a malfunction, prompt injection, or misconfiguration. The GH-600 study guide explicitly lists 'Configure an agent's scope to a specific repository' as a tool and environment interaction skill. The other options describe billing, model selection, and PR review rules, which are unrelated to scoping.
9In the GH-600 evaluation domain, what is the role of 'automated scanning tools' in generating evaluation signals for agent tasks?
A.They replace the need for human code review by automatically approving pull requests
B.They produce machine-readable artifacts such as test results, code analysis reports, and security scans that objectively measure agent output quality
C.They monitor GitHub API rate limits to ensure agents do not exceed usage quotas
D.They generate synthetic training data to fine-tune the underlying language model
Explanation: Automated scanning tools (such as test runners, linters, SAST tools, and dependency scanners) generate structured, machine-readable outputs that serve as quantitative evaluation signals for agent tasks. The GH-600 study guide explicitly mentions using automated scanning tools to generate evaluation signals and align them with development intent. The other options describe PR auto-approval, API quota monitoring, and model fine-tuning, which are not the purpose of scanning tools in this context.
10An agent repeatedly misuses a tool by calling it with incorrect parameters, causing downstream workflow failures. According to the GH-600 study guide, how should this failure be classified?
A.A reasoning error caused by incorrect model weights
B.A context or environment issue caused by insufficient compute resources
C.A tool misuse root cause requiring revision of tool instructions, constraints, or access configuration
D.A memory expiration issue requiring longer context retention
Explanation: The GH-600 study guide categorizes agent failures into reasoning errors, tool misuse, and context/environment issues. Repeatedly calling a tool with incorrect parameters is a classic tool misuse failure, which should be addressed by revising the agent's tool instructions, refining usage constraints, or adjusting tool access. Model weights and compute resources are infrastructure concerns outside the scope of agent configuration.

About the GitHub Agentic AI Developer Exam

GitHub Certified: Agentic AI Developer (GH-600) validates the ability to operate, supervise, and govern autonomous AI agents inside production-grade GitHub SDLC workflows. The exam covers six domains: preparing agent architecture and integrating agents into the SDLC, implementing tool use and MCP server configuration, managing memory and state, performing evaluation and error analysis, orchestrating multi-agent coordination, and implementing guardrails and accountability. The beta exam launched in early 2026 with GA planned for July 2026.

Questions

50 scored questions

Time Limit

120 minutes

Passing Score

700/1000

Exam Fee

TBD (GA July 2026) (GitHub (Microsoft))

GitHub Agentic AI Developer Exam Content Outline

15–20%

Prepare Agent Architecture and SDLC Processes

Define agent task boundaries, configure planning/execution separation, output structured plans, validate plans before execution, and configure observability with human-intervention controls

20–25%

Implement Tool Use and Environment Interaction

Select and configure tools, configure MCP servers and allow lists, add remote GitHub MCP servers, manage MCP registries, scope agents to repositories and branches, invoke agents in CI, implement retry/rollback/escalation, and ensure traceability

10–15%

Manage Memory, State, and Execution

Choose short-term, long-term, or external memory; scope memory to task-relevant information; define expiration and pruning rules; capture durable artifacts; detect and correct context drift; share state and prevent conflicting or stale context

15–20%

Perform Evaluation, Error Analysis, and Tuning

Define success criteria and evaluation signals, use automated scanning tools, identify failures from logs/plans/traces, classify root causes (reasoning errors, tool misuse, context/environment issues), and tune instructions, memory, and tool access

15–20%

Orchestrate Multi-Agent Coordination

Apply orchestration patterns, configure agent isolation, detect and resolve agent conflicts, configure multi-agent observability and artifact production, document decisions and handoffs, detect and respond to multi-agent failures, and manage agent lifecycle

10–15%

Implement Guardrails and Accountability

Classify actions by risk, assign autonomy levels, block policy-violating actions, scope permissions to least-privilege, require explicit authorization for irreversible or compliance-sensitive changes, and preserve velocity by minimizing unnecessary approvals

How to Pass the GitHub Agentic AI Developer Exam

What You Need to Know

  • Passing score: 700/1000
  • Exam length: 50 questions
  • Time limit: 120 minutes
  • Exam fee: TBD (GA July 2026)

Keys to Passing

  • Complete 500+ practice questions
  • Score 80%+ consistently before scheduling
  • Focus on highest-weighted sections
  • Use our AI tutor for tough concepts

GitHub Agentic AI Developer Study Tips from Top Performers

1Focus first on the highest-weighted domain (20–25%): MCP server configuration, allow lists, remote MCP servers, and MCP registries
2Understand the six domains cold and which skills belong to which domain — GH-600 frequently tests domain attribution
3Practice the planning-before-execution pattern: define inputs/outputs/success criteria, output a structured plan, validate it, then execute
4Know the three root-cause categories for agent failures: reasoning errors, tool misuse, and context/environment issues — and the tuning action for each
5Memorize the guardrails principle: irreversible or compliance-sensitive changes require explicit authorization; low-risk reversible actions can proceed autonomously
6Understand memory strategy selection: short-term (in-context), long-term (persistent external), and when to use each in long-running vs. short tasks
7Study multi-agent conflict types: overlapping code changes, duplicated effort, and contradictory outputs — and the isolation and orchestration patterns that prevent them
8Complete 100+ practice questions across all six domains and review every wrong answer until you can explain the GH-600 principle behind the correct choice

Frequently Asked Questions

What is the GH-600 passing score?

The GitHub Certified: Agentic AI Developer exam (GH-600) requires a scaled score of 700 out of 1000 to pass, consistent with Microsoft's standard certification threshold. The exam contains approximately 40–60 scenario-based questions delivered in 120 minutes.

Is GH-600 available now or is it still in beta?

The GH-600 beta exam ran through May 31, 2026, with general availability (GA) planned for July 2026. Beta participants who took the exam on or before May 31, 2026 received an 80% discount on the exam fee. After GA, the exam is available to all candidates at standard pricing.

What does the GH-600 exam cover?

GH-600 covers six domains: (1) preparing agent architecture and integrating agents into the SDLC (15–20%), (2) implementing tool use and MCP server configuration (20–25%), (3) managing agent memory, state, and execution (10–15%), (4) performing evaluation, error analysis, and tuning (15–20%), (5) orchestrating multi-agent coordination (15–20%), and (6) implementing guardrails and accountability (10–15%). MCP server configuration carries the highest weight and is central to the exam.

What prior experience do I need for GH-600?

GitHub recommends experience with the full software development lifecycle (SDLC) on GitHub including repositories, Actions, pull requests, and branch protection. You should also have hands-on experience with GitHub Copilot — especially agent mode, custom instructions, MCP servers, and custom agents. Prior GitHub Foundations and GitHub Copilot (GH-300) certification or equivalent experience is strongly recommended.

How long should I study for GH-600?

Most candidates with prior GitHub and Copilot experience require 4–8 weeks (40–80 hours) to prepare. Allocate significant time to hands-on practice: configure MCP servers, build agent workflows in GitHub Actions, and practice multi-agent coordination patterns. Use Microsoft Learn's free learning paths: 'Foundations of Agentic AI in GitHub' and 'Tooling, MCP, and Agent Execution Environments' are directly aligned to the exam.

What is Model Context Protocol (MCP) and why is it important for GH-600?

MCP (Model Context Protocol) is an open standard that allows GitHub Copilot agents to connect to external tools, APIs, and data sources. GH-600 tests configuration of MCP servers, allow lists, remote GitHub MCP servers, and MCP registries. MCP server configuration sits in the highest-weighted exam domain (20–25%), making it the most critical technical skill for passing the exam.

Does GH-600 expire and how do I renew it?

Yes. GitHub certifications following the Microsoft certification renewal model expire annually. You can renew for free by passing a free online assessment on Microsoft Learn before your certification expires. No proctored re-examination or additional fee is required for annual renewal.