GH-300: GitHub Copilot is GitHub's certification for using Copilot well and safely. It covers Copilot in the IDE and on the command line, agent mode and MCP, code review, prompt engineering, how Copilot handles your data, and the organisation settings that keep code private. Employers increasingly expect developers to use AI coding assistants responsibly, so this is a useful credential for freshers and working developers alike.
This guide follows the GH-300 study guide (skills measured as of 7 August 2026): every skill area, prompt examples, a 3-week plan and 20 original practice questions.
Quick facts (Microsoft Learn, November 2026)
| Item | Detail |
|---|---|
| Exam | GH-300: GitHub Copilot |
| Level | Intermediate |
| Time | 100 minutes; proctored; may include interactive components |
| Passing score | 700 |
| Languages | English, Spanish, Portuguese (Brazil), Korean, Japanese |
| Valid for | 2 years; GitHub is moving to Microsoft's recertification process |
| Practice | Free Practice Assessment on Microsoft Learn |
| Price | Set by the country where you take the exam; scheduled through Pearson VUE |
Read the official GitHub Copilot certification page and GH-300 study guide before booking.
Who should take GH-300?
- Developers who use Copilot daily and want to prove they use it well.
- Freshers who want to show AI-assisted development skills on their résumé.
- Team leads and admins who roll out Copilot and manage its policies.
You should know GitHub basics (repositories, pull requests) and at least one programming language. Pair it with GH-200 GitHub Actions for a DevOps profile.
Skills measured and weights
| Skill area | Weight |
|---|---|
| Use GitHub Copilot responsibly | 15–20% |
| Use GitHub Copilot features | 25–30% |
| Understand GitHub Copilot data and architecture | 10–15% |
| Apply prompt engineering and context crafting | 10–15% |
| Improve developer productivity with GitHub Copilot | 10–15% |
| Configure privacy, content exclusions, and safeguards | 10–15% |
(Microsoft's page currently also shows a second "GitHub Copilot features" heading with no skills under it. The six areas above are the ones with content.)
1. Use Copilot responsibly (15–20%)
- Risks and limits of generative AI tools; ethical and responsible use; potential harms and how to reduce them.
- Why you must validate AI output: review, test and check security and licensing before you merge.
2. Use Copilot features (25–30%)
- In the IDE: inline suggestions, chat, CLI and agent mode; content exclusions for files and repositories.
- Copilot CLI: what it is, how to install it, key commands, interactive sessions, generating scripts and managing files.
- Capabilities: agent mode, Copilot Edits and MCP; agent sessions and sub-agents; Copilot code review; Spaces, Spark, pull request summaries and review standards set in instructions files; Copilot Chat limits, commands and reusable prompt files.
- Organisation settings: policies, Copilot code review policies, feature availability across IDEs and github.com, audit log events, and managing subscriptions through the REST API.
3. Data and architecture (10–15%)
- How data flows: input processing and prompt building, the proxy that filters requests, and post-processing of suggestions.
- The lifecycle of a code suggestion and the limitations of LLMs and Copilot (outdated knowledge, plausible but wrong code).
4. Prompt engineering and context (10–15%)
- Prompt structure and how Copilot decides what context to use (open files, selection, workspace, instructions files).
- Zero-shot and few-shot prompting; best practices; how chat history is used.
// Weak prompt
// write a function for dates
// Strong prompt: goal, inputs, outputs, constraints and an example
// Write a TypeScript function toIstDate(utc: string): string that converts an ISO UTC
// timestamp to an IST date "DD-MM-YYYY". Do not use external libraries.
// Example: "2026-11-13T20:30:00Z" -> "14-11-2026"5. Developer productivity (10–15%)
- Code generation, refactoring and documentation; learning unfamiliar code faster; generating sample data; modernising legacy code.
- Unit and integration tests, edge cases and assertions; security and performance suggestions.
6. Privacy, exclusions and safeguards (10–15%)
- Content exclusions and editor settings; who owns the output and its limitations.
- The filter that blocks suggestions matching public code; troubleshooting missing suggestions and exclusions.
3-week study plan
| Days | Focus | Practice |
|---|---|---|
| 1–3 | Responsible AI, Copilot plans, data flow and limitations | Write down 5 ways a suggestion could be wrong or unsafe |
| 4–7 | IDE suggestions, chat, slash commands, prompt files | Build a small CRUD API using only prompts, then review every line |
| 8–10 | Agent mode, Copilot Edits, MCP, Copilot CLI | Ask agent mode to add a feature across several files; check the diff |
| 11–13 | Tests, refactoring, documentation, legacy modernisation | Generate unit tests with edge cases for an existing project |
| 14–17 | Organisation policies, content exclusions, public-code filter, audit logs | Configure an exclusion for a secrets folder in a test organisation |
| 18–21 | Practice Assessment, revision | Book the exam |
20 practice questions with answers
These are original practice questions written for this guide, not exam questions.
1. Copilot suggests code that compiles. Do you still need to review it?
Yes. AI output must be validated for correctness, security and licensing before use.
2. Copilot must never read files in a config/secrets folder. What do you configure?
A content exclusion for that path.
3. Your company wants to block suggestions that match public code. What setting?
The policy that blocks suggestions matching public code.
4. Copilot should change several files to add a feature, run commands and iterate. Which mode?
Agent mode.
5. Copilot needs to call an internal tool, such as a ticketing system, during a session. What protocol connects it?
MCP (Model Context Protocol).
6. You want Copilot in the terminal to suggest and explain shell commands. What do you use?
GitHub Copilot CLI.
7. Every pull request should get an AI-generated description of its changes. Which feature?
Copilot pull request summaries.
8. Reviewers want Copilot to check PRs against team coding standards. Where are the standards defined?
In custom instructions files used by Copilot code review.
9. Your team repeats the same detailed prompt for generating API tests. How do you reuse it?
Save it as a prompt file.
10. What is few-shot prompting?
Giving the model a few examples of the input and output you want before asking for the result.
11. Copilot's suggestion ignores your project's naming style. What helps?
Better context: open related files, add examples, or set repository custom instructions.
12. Which component filters prompts and suggestions between the IDE and the model?
The Copilot proxy service, which also applies post-processing filters.
13. Why might Copilot suggest an outdated library API?
LLMs are trained on past data and can produce plausible but outdated or wrong code.
14. An admin must see who enabled Copilot features in the organisation. Where?
Audit log events.
15. You must assign Copilot seats to 200 users automatically. What do you use?
The REST API for Copilot subscription management.
16. How can Copilot help with a 15-year-old Java codebase?
Explain unfamiliar code, suggest refactorings, generate tests before changes, and help modernise APIs, with human review.
17. You need realistic test data for a customer table. Can Copilot help?
Yes. It can generate sample data, which you should check to make sure no real personal data is used.
18. Copilot stops suggesting in one repository. What do you check?
Content exclusions, policies and seat assignment, the extension status, network or proxy settings, and the file type.
19. Who is responsible for code that Copilot suggested and you merged?
You and your team. Treat Copilot output like any other code you commit.
20. What should a good prompt include?
The goal, inputs and outputs, constraints, and ideally an example.
After GH-300
- Read GitHub portfolio projects for freshers and build AI projects from 25 agentic AI projects.
- Learn MCP hands-on with the MCP server Python tutorial.
- See live jobs for freshers.
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Frequently Asked Questions
How long is the GitHub Copilot (GH-300) exam?
100 minutes. It is proctored and may include interactive components.
What is the passing score for GH-300?
700.
How long is the GitHub Copilot certification valid?
2 years. GitHub is moving to Microsoft's recertification process, and certifications expiring before it launches get a 6-month extension.
Which GH-300 area has the most weight?
Use GitHub Copilot features, at 25–30%.
Does GH-300 cover agent mode and MCP?
Yes. The skills measured since 7 August 2026 include agent mode, Copilot Edits, MCP, agent sessions, Copilot CLI and Copilot code review.
Is there a free practice test for GH-300?
Yes. Microsoft Learn offers a free Practice Assessment for the GitHub Copilot certification.
