GH-300 — GitHub Copilot Certification
Your hub for the GitHub Copilot exam: what it tests, the study aids built into this course, and a full 529-question simulator. The concepts behind it live across the rest of the site — this is where you aim them at the cert.
This is an independent, third-party study resource — not affiliated with, endorsed by, or an official product of GitHub or Microsoft. “GH-300” and “GitHub Copilot” are trademarks of their owners, used here only to describe what this course helps you study. The practice questions are original study material, not real exam content. Always confirm current exam details on the official certification page.
GH-300 is a test that gives you a badge to prove you know how to use GitHub Copilot — a robot helper that writes computer code with you — in a smart and safe way. It’s like earning a swimming badge: it shows grown-ups you can do it properly, not just splash around.
GH-300 validates that you can use GitHub Copilot responsibly, efficiently, and securely across the software lifecycle — not just write functions faster. It leans heavily on governance and data-handling, not model theory.
| Logistics | |
|---|---|
| Cost | $99 USD · proctored via Pearson VUE (online or test center) |
| Format | ~100 minutes · multiple-choice + scenario-based |
| Pass | 700 / 1000 |
| Validity | 2 years · free renewal via a Microsoft Learn assessment |
| Level | Intermediate · no formal prerequisites (hands-on Copilot use strongly recommended) |
The six domains (skills measured — Jan 2026 update)
☺ Like you’re 10: The test is split into six topic buckets, like the different subjects on a report card — some are about being fair and safe, some about asking the helper good questions, and some about actually using its features. The “using the features” bucket is the biggest one.
| Domain | Weight | Core of it |
|---|---|---|
| Use GitHub Copilot features | 25–30% | Inline suggestions, chat, CLI & Plan Mode; Agent & Edit modes, MCP, Agent Sessions & Sub-Agents; code review; Spaces & Spark; org-wide policy & audit logs |
| Use GitHub Copilot responsibly | 15–20% | Risks & limits of generative AI, ethical use, potential harms & mitigations, validating and operating output responsibly |
| Understand data & architecture | 10–15% | Data usage, flow & sharing; prompt building; proxy filtering & post-processing; the suggestion lifecycle; LLM limits |
| Apply prompt engineering & context crafting | 10–15% | Prompt structure & context, how context is determined, zero- and few-shot, prompt-crafting best practices |
| Improve developer productivity | 10–15% | Generation, refactoring & docs; sample data & legacy modernization; unit & integration tests and edge cases; security & performance suggestions |
| Configure privacy, content exclusions & safeguards | 10–15% | Content exclusions & editor settings, output ownership & limits, duplication detection & security warnings, troubleshooting |
Weights are the official ranges from Microsoft’s GH-300 skills-measured guide (Jan 2026 update) — Use GitHub Copilot features is the largest at 25–30%. Always confirm the current split on the official guide.
Foxy: My tummy’s doing flips! There are so many questions — how do I even start on GH-300?
Professor Owl: Breathe, Foxy. First, place each scenario on the map: which of the six buckets is it in — is it a plan-and-features rule, a privacy rule, or a responsible-AI one?
Timmy the Turtle: Then read every single option slowly. The tricky ones swap “Business+ only” for “all plans,” and one careful re-read catches it.
Foxy: Oh! So I locate the bucket first, then check each choice against the rule. That feels way less scary.
Professor Owl: Exactly. Name the domain, weigh the wording, pick the safest, most responsible answer. You’ve got this.
Content exclusions are Business+ only (and don’t apply to the CLI, cloud agent, or Agent mode); the duplication detector compares the suggestion + ~150 chars against the public-code index; the developer bears final responsibility for accepted code; and you must know Microsoft’s six Responsible AI principles by name. The one-page exam-day sheet has them all.
The exam is really a set of course concepts wearing GitHub Copilot names. Here’s the map from what you learned to how GH-300 asks about it.
☺ Like you’re 10: Every big idea you learned — like a helper that plans and fixes its own mistakes, or the rules that keep your code private — has a special GitHub Copilot name. This table is the decoder ring: your idea on the left, its Copilot name on the right.
| Course concept | In GitHub Copilot terms (GH-300) |
|---|---|
| The agent loop | Copilot Agent mode — plans, runs terminal/tools, reads errors, and iterates |
| Chat interaction modes | Ask / Edit / Agent / Plan Mode in Copilot Chat |
| Prompting | Single-responsibility prompts, #-context, slash-commands, and prompt files |
| Custom instructions & memory | copilot-instructions.md (repo & path-scoped), personal instructions, Copilot Spaces |
| Tools & MCP | Model Context Protocol servers that give Copilot extra tools and data |
| Cloud / async agents | The Copilot coding (cloud) agent — assign an Issue, it opens a PR |
| Models & local LLM | The model picker; frontier & reasoning models; local-model trade-offs |
| Governance & data | Content exclusions, org/enterprise policy, audit logs, the metrics APIs |
| Responsible AI | Fairness, transparency, matching-code/IP, human oversight, privacy |
Drill it
The interactive aids share one question bank — filter any of them to ★ GH-300 scope:
- Self-Check — instant-feedback quiz drawn from the shared bank; filter to ★ GH-300 for the cert-scoped set (15 questions), or drop the filter for all 135.
- Flashcards — flip deck of the key terms; filter to ★ GH-300.
- Lab Track — a checkable path so the scenario questions feel familiar (hands-on is what the exam rewards).
- Common Mistakes — the gotchas the exam likes to test.
- Reference — the GH-300 study map, glossary, and the which-model-when table.
- PDFs (in the course download): exam-day.pdf (cert-scoped one-pager) and cheat-sheet.pdf (the whole course).
A self-contained study program (pick 2, 3, or 4 weeks) that paces you from the concepts through the labs, flashcards, and self-checks to a timed simulator run — one checkable path so you know what to do each day.
Read the relevant topics → run the labs → drill flashcards (★ GH-300) daily → take the Self-Check → then the full simulator under timed conditions. Re-read the exam-day sheet the morning of.
A full, self-contained 529-question exam simulator with three modes:
- Exam mode — timed, full-length, Pearson-VUE-style, with a question minimap and a scored result.
- Practice mode — no timer, immediate feedback and explanations as you go.
- Domain mode — drill a single domain to shore up a weak area.
It tracks performance by domain and gives a full answer review at the end.
The simulator is a standalone app included in the course download. If you’re viewing the single-file build on its own, keep gh300-simulator.html in the same folder for the launch link to work.
Go to the source — these are the authoritative materials:
- Official study guide (the exam blueprint): learn.microsoft.com · GH-300 study guide.
- Certification page (overview, exam sandbox, practice assessment): learn.microsoft.com · GitHub Copilot.
- GitHub Copilot Fundamentals (free Microsoft Learn path, Parts 1 & 2) — linked from the certification page; the official baseline.
- Product docs: docs.github.com/copilot — especially content exclusion, data handling, and plan comparison.
The exam was substantially revised in Jan 2026 and keeps evolving. Anything in this hub is a snapshot — confirm scope and policies against the official study guide before test day.
If you’re learning AI broadly, the CCAR-F hub covers Anthropic’s Claude certification — many concepts (agents, MCP, tools, evals) overlap directly.