GitHub Copilot Certification (GH-300): Study Guide and 3-Week Plan

The GitHub Copilot certification is the credential you earn by passing Exam GH-300: GitHub Copilot, a 100-minute proctored exam that costs $99 in the US and needs a scaled score of 700 to pass. It checks whether you can use Copilot well and safely: its features in the IDE, the CLI and on github.com, prompt and context crafting, how Copilot handles your data, and the privacy and policy settings that organization admins control.

This guide walks through the published GH-300 skills outline one domain at a time, then gives you a 3-week study plan (two weeks if you already use Copilot every day) and hands-on drills you can run in Copilot itself. GitHub exams are delivered through Microsoft Learn and Pearson VUE, so the official facts below come from Microsoft Learn.

GH-300 at a glance

Checked against Microsoft Learn’s GitHub Copilot certification page and GH-300 study guide on October 6, 2026.

Outside the US, Microsoft’s exam pricing data lists GH-300 at £64 in the UK, €76 in Germany, ₹3,691 in India, ¥12,180 in Japan and US$84 in Australia and Canada, before tax. Our guide to what AI certifications really cost and the AI certification cost calculator help you budget for retakes. The certification page also links Microsoft’s Exam Replay offer: one exam voucher plus one retake of the same exam, both used within 12 months.

What the GitHub Copilot certification proves

Microsoft’s audience profile says GH-300 candidates should be experts at using GitHub Copilot to improve software development productivity, quality and security. That covers responsible AI use, prompt engineering, Copilot features across the different plans, and privacy safeguards. You should also know GitHub fundamentals and have written code in at least one language.

Read that list closely and you’ll notice GH-300 is not only a “power user” exam. A good share of the outline is about settings and controls, many of them managed by organization owners: policies, audit log events, seat management, content exclusions and the public code filter. If you have only ever used Copilot as an individual, those admin topics are where most of your study time will go.

The six GH-300 domains and their weights

One oddity: the “skills at a glance” list on Microsoft Learn shows the features line twice (“Use GitHub Copilot features” and “GitHub Copilot features”, both 25–30%). The detailed outline underneath has six domains, so study it as six.

Microsoft adds two notes that apply to every domain. The bullets under each skill illustrate how it is assessed, and related topics may also be covered. And most questions cover generally available features, though Preview features can appear if they are commonly used. The August 7, 2026 update made minor changes to the IDE group, the features and capabilities group, and the safeguards group.

Domain 1: Use GitHub Copilot responsibly (15–20%)

What the published outline lists:

• Responsible AI principles: the risks and limitations of generative AI tools, ethical and responsible use, and potential harms with ways to mitigate them.
• Validating and operating AI tools: why AI output needs validating, and how to operate GitHub Copilot responsibly.

How to study it: start with the “Responsible AI with GitHub Copilot” module, the first one in GitHub Copilot Fundamentals Part 1. Then turn it into habits you can explain: treat every suggestion like a pull request from a new teammate, run the tests, check that generated code does what you asked and nothing more, and know when a suggestion could carry security or licensing risk. This domain rewards judgment more than feature knowledge. Our explainers on why AI makes things up and the AI fact-check workflow cover the same ideas outside of code.

Domain 2: Use GitHub Copilot features (25–30%)

The biggest domain, and the one that changes most often as Copilot ships new features.

What the published outline lists:

• Copilot in the IDE: enabling it, triggering it through inline suggestions, chat, the CLI and agent mode, and configuring content exclusions for specific files or repositories.
• GitHub Copilot CLI: what it is and why it helps, installing it, its key features and commands, using it interactively and in sessions, and generating scripts and managing files with it.
• Features and capabilities: agent mode, Copilot Edits and MCP; managing agent sessions and delegating tasks to sub-agents to use context efficiently; Copilot for code review; Spaces, Spark, pull request summaries and review standards set through instructions files; and Copilot Chat’s limits, options, feedback and commands, including reusable prompt files.
• Organization-wide settings and policies: org-wide policy management, Copilot code review policies, feature availability across IDEs and github.com, audit log events, and managing subscriptions with the REST API.

How to study it: touch every feature at least once, on purpose. Install the Copilot CLI and use it for a real task, such as writing a script you actually need. Give agent mode a small, well-scoped change and review exactly what it did. Add an MCP server, write a repository instructions file and a reusable prompt file, and compare Copilot’s answers before and after. Ask Copilot to review a pull request. GitHub Copilot Fundamentals Part 2 covers agent mode, the cloud agent, the GitHub MCP Server and code reviews.

For the organization settings, you need an org owner’s view. If you are not one, read GitHub’s documentation for Copilot policies, audit log events and the REST API endpoints for seat management, and write down where each setting lives and who can change it.

Domain 3: Understand GitHub Copilot data and architecture (10–15%)

What the published outline lists:

• Data handling and flow: how data is used, where it flows and how it is shared; how input is processed and the prompt is built; proxy filtering and post-processing.
• Lifecycle and limitations: the lifecycle of a code suggestion, and the limitations of large language models and of Copilot.

How to study it: draw the pipeline on one page. Start with what is in your editor, then the prompt Copilot builds from it, the filtering on the way to the model and on the way back, and finally the suggestion you accept or reject. If you can explain each stage in one sentence, you have this domain. The study guide points to the GitHub Trust Center for how Copilot handles data, and our plain-English piece on how AI chatbots really work covers the model side.

Domain 4: Apply prompt engineering and context crafting (10–15%)

What the published outline lists:

• Crafting effective prompts: prompt structure and context, how context is determined, zero-shot and few-shot prompting, and best practices for prompt crafting.
• Engineering prompts for performance: prompt engineering principles, and the prompt process flow, including how chat history is used.

How to study it: read GitHub’s prompt engineering guide for Copilot Chat and the “Introduction to prompt engineering with GitHub Copilot” module. Then drill: ask for the same function three ways (a vague one-liner, a specific request with constraints, and a request with two worked examples) and compare the results. Repeat with different files open and notice how the answer changes. That experiment teaches “how context is determined” better than any reading. Our complete guide to AI prompting and Prompt Refinement 101 cover the general principles.

Domain 5: Improve developer productivity with GitHub Copilot (10–15%)

What the published outline lists:

• Productivity and code quality: code generation, refactoring and documentation; learning faster with less context switching; generating sample data and modernizing legacy code.
• Testing and security: generating unit and integration tests, finding edge cases and writing assertions, and suggesting security improvements and performance optimizations.

How to study it: this is the domain most daily users already know, so make it deliberate. Take an old, messy function and ask Copilot to explain it, refactor it, document it, write unit tests that cover the edge cases, and suggest security fixes. Check each step yourself. The Microsoft Learn modules “Developer use cases for AI with GitHub Copilot” and “Develop unit tests using GitHub Copilot tools” match this domain closely.

Domain 6: Configure privacy, content exclusions, and safeguards (10–15%)

What the published outline lists:

• Privacy settings and exclusions: configuring content exclusions and editor settings, and the ownership and limitations of outputs.
• Safeguards and troubleshooting: enabling the filter for suggestions that match public code, and resolving problems with suggestions and content exclusions.

How to study it: read GitHub’s content exclusion documentation end to end, because the details are the point here. GitHub says content exclusion is available on the Copilot Business and Copilot Enterprise plans, and that repository administrators, organization owners and enterprise owners can configure it. Its docs also list where exclusions do not apply (for example, GitHub currently says Copilot CLI and agent mode in IDE chat do not support content exclusion) and note that a change can take up to 30 minutes to reach an IDE that already loaded its settings. That kind of detail is exactly what “resolve issues with content exclusions” is about. Then find the public code filter setting and know what turning it on changes.

A 3-week GH-300 study plan

Plan on about an hour a day on weekdays plus one longer hands-on session each weekend. The order front-loads the habits and prompting skills you will use in every later lab, then spends the most time on Domain 2, the heaviest.

Two-week version: if Copilot is already part of your daily work, compress Week 1 into three days and spend the time you save on Domain 2’s org settings and Domain 6’s exclusions, the topics daily users most often have never touched. Want a plan fitted to your own calendar? The free AI certification study plan generator builds one.

Hands-on practice using Copilot itself

The best GH-300 lab is Copilot. Set up a small practice repository in a language you know (the Part 2 learning path includes modules for JavaScript and Python if you want a starting point) and run these drills:

1. One surface a day. Inline suggestions, Copilot Chat, agent mode, the Copilot CLI and Copilot code review. Note what each is best at and where it falls short.
2. Instructions and prompt files. Write a repository instructions file with your coding standards and a reusable prompt file for a common task. Compare answers with and without them.
3. The context experiment. Ask the same question with different files open and different selections. Write down what changed and why.
4. The legacy rescue. Pick old code, then explain, refactor, document and test it with Copilot. Review every change yourself.
5. The edge-case hunt. Ask Copilot for unit and integration tests, then ask it which edge cases it missed. Add assertions for the ones that matter.
6. The settings tour. Turn on the public code filter. If your organization uses Copilot Business or Enterprise, ask an admin to walk you through content exclusions, policies and the audit log.
7. Quiz yourself. Paste one domain of the published outline into Copilot Chat and ask for original practice questions with explanations. Check every answer against GitHub’s docs, because AI-written questions can be wrong.

Which plan do you need? GitHub’s plans page says Copilot Free gives limited access to a selection of features (inline suggestions are capped at 2,000 completions a month), verified students can get Copilot Student, and verified teachers and maintainers of popular open source projects may qualify for free Copilot Pro. Organization-level settings such as policies and content exclusions need an organization plan, so check what your plan includes before you plan your labs.

Skip any site selling leaked GH-300 questions. Those are dumps, and they teach you answers instead of skills. HOW TO // AI does not have a GH-300 practice bank yet; for the AI certifications we do cover, our free practice exams are on AI Exam Prep →

Free official prep resources

• GH-300 study guide: the full skills outline, the change log and links to GitHub documentation for each domain.
• Practice Assessment: free on the certification page. Microsoft describes it as an overview of the style, wording and difficulty of the questions; it is not a copy of the exam.
• Exam sandbox: a demo of the GitHub exam interface and question types, so nothing surprises you on the day.
• GitHub Copilot Fundamentals Part 1 of 2: nine modules, including responsible AI, prompt engineering, Copilot Spaces, Copilot across environments, management and customization, and unit tests.
• GitHub Copilot Fundamentals Part 2 of 2: six modules, including agent mode, the cloud agent, the GitHub MCP Server and code reviews.

What exam day looks like

• 100 minutes, proctored, with possible interactive components. Five minutes of break time are built in; the clock keeps running during a break, and you cannot go back to questions you saw before it.
• No Microsoft Learn during the exam. Microsoft lets candidates open Learn during associate and expert exams, but not during GitHub exams.
• Language time. If GH-300 is not offered in your preferred language, you can request an extra 30 minutes.
• Use a personal Microsoft account to register. Microsoft warns that exam records tied to a work or school account are lost if you leave that organization.
• Retakes: wait 24 hours after a first fail; later waits follow Microsoft’s retake policy.

Source for the break and Learn-access rules: Microsoft’s exam duration and exam experience page.

Who GH-300 is for

Microsoft Learn tags the certification for developers, DevOps engineers, app makers and technology managers. In practice it fits:

• Developers who use Copilot every day and want proof they use it well, not just often.
• Team leads and DevOps engineers rolling Copilot out to a team, who need the policy, privacy and exclusion knowledge anyway.
• Technology managers who approve Copilot and want to understand what they are approving.

It is a weaker fit if you do not write code: the outline assumes experience with at least one programming language. Business users of Microsoft 365 Copilot are better served by Microsoft’s AB-730 (AI Business Professional), covered in our Microsoft AI certifications guide. For more developer options, see the best AI certifications for software developers.

GH-300 vs GH-600 and Microsoft’s AI exams

GH-300 vs GH-600. GH-600 (Developing in Agentic AI Systems) is GitHub’s newer certification. Where GH-300 asks whether you can use and configure Copilot, GH-600 asks whether you can operate, supervise and govern AI agents inside production development workflows, with GitHub as the system of record and control plane. Its six domains are agent architecture and SDLC processes (15–20%), tool use and environment interaction (20–25%), memory, state and execution (10–15%), evaluation, error analysis and tuning (15–20%), multi-agent coordination (15–20%), and guardrails and accountability (10–15%). It runs 120 minutes, costs $165 in the US and is offered in English only.

Microsoft lists no required certification for GH-600, but its study guide expects experience with coding agents including GitHub Copilot, MCP servers, custom instructions, custom agents and Copilot setup steps. That makes GH-300 a sensible first step: most of what GH-600 assumes you already know is on the GH-300 outline.

GH-300 vs Microsoft’s AI exams. None of Microsoft’s Azure AI certifications test GitHub Copilot. AI-901 is about AI concepts and Microsoft Foundry, and AI-103 is about building AI apps and agents on Azure in code (our AI-901 vs AI-103 comparison explains the split). A developer who builds AI features and uses Copilot to write them could reasonably hold GH-300 and AI-103. If you build on Claude instead, see the Claude Certified Developer: Foundations (CCDV-F) study guide. Every option is in our AI certification index and the generative AI certifications guide →

One more to watch: Microsoft’s June 2026 credentials roundup announced a GitHub Copilot Pro Badge based on “Verified Proficiencies”, earned from evidence in your everyday work rather than a separate exam. Microsoft said it would become generally available at GitHub Universe in late October 2026. It is a different credential from the GH-300 certification.

After you pass: validity and renewal

GitHub certifications are valid for 2 years. GitHub is moving to Microsoft’s recertification process, which will let you keep a certification current without retaking the full exam. Until that process is available, any GitHub certification that would expire gets a 6-month extension, and GitHub says people whose certification has already expired can request one exam voucher for their first renewal attempt (contact details are on the certification page). Note that the GH-300 study guide repeats Microsoft’s generic line that associate certifications expire every year; the certification page’s 2-year rule is the one that applies to GitHub certifications.

Our guide to AI certification renewal rules and the renewal tracker help you keep the date.

HOW TO // AI is not affiliated with or endorsed by GitHub or Microsoft. GitHub and GitHub Copilot are trademarks of GitHub, Inc.; Microsoft, Microsoft Learn, Azure and Microsoft 365 are trademarks of Microsoft Corporation. We reference them descriptively. All content is original and based on the published study guide. Check the official certification page before you book.

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