GH-300 (GitHub Copilot) is a single 100-minute Pearson VUE exam, roughly 60 scored questions plus unscored pretest items, passing score 700 out of 1000. Microsoft doesn't publish a fixed USD price for GH-series exams — it's set by the country or region where you're proctored. Content was last revised August 7, 2026. Six domains are tested, from responsible AI use (15–20%) through hands-on Copilot features (25–30%, the single largest domain) down to prompt engineering, productivity, and privacy configuration — and the domain candidates most often shortchange isn't the biggest one, it's data and architecture, because almost nobody thinks about that layer during normal daily use.
Exam Snapshot
Sourced from Microsoft's official GH-300 study guide and certification page, fetched September 7, 2026. GH-300 is one of the few GitHub-branded exams that lives directly on Microsoft's own credentials platform and is scheduled through Pearson VUE — GH-900 (GitHub Foundations), by contrast, is scheduled through PSI. Don't assume the two exams share a registration flow.
Use GitHub Copilot Responsibly (15–20%)
This domain has two parts: understanding responsible AI principles (risks and limitations of generative AI, ethical usage, potential harms and mitigation strategies) and validating and operating AI tools (why AI output needs validation, and how to operate Copilot responsibly day to day). Both trace back to Microsoft's Responsible AI Standard, which names six principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability.
Where candidates trip: transparency and accountability get swapped constantly. Transparency is about a system being understandable and auditable to the people using it. Accountability is about the people who built the system remaining responsible for its behavior and monitoring it over time. One is about the AI being legible; the other is about a human owning the consequences.
Study tactic: write all six principles down with one distinguishing verb each — fair (treat groups equally), reliable/safe (don't cause harm), private/secure (don't leak data), inclusive (empower more people), transparent (be understandable), accountable (someone owns it). Exam questions describe a scenario and ask which single principle it best illustrates; memorized one-word anchors resolve these faster than re-deriving definitions from scratch.
Use GitHub Copilot Features (25–30%)
This is the largest domain on the exam by a wide margin, and it covers four distinct surfaces: Copilot in the IDE (enabling it, triggering suggestions through inline completions, chat, CLI, and agent mode), the Copilot CLI (installation, interactive and session use, generating scripts and managing files from the terminal), the newer feature set (Agent Mode, Copilot Edits, MCP for tool integration, Sub-Agents and Agent Sessions for delegating work, code review, Spaces, Spark, PR summaries, and reusable instructions/prompt files), and organization-wide administration (policy management including Copilot Code Review policy, feature availability control across IDEs and github.com, audit log events, and managing subscriptions via the REST API).
Where candidates trip: Agent Mode, Copilot Edits, the Copilot CLI, and Sub-Agents all sound like overlapping ways to get Copilot to "just do the thing," and the exam leans on scenario questions that require picking the right one for a specific workflow, not defining any of them in isolation.
Study tactic: build your own one-line mapping of task type to Copilot surface before you touch a single practice question — see the GH-300 cheat sheet's decision table for a starting point. Because this domain is worth nearly a third of the exam, a wrong mental model here costs more than in any other section.
Understand GitHub Copilot Data and Architecture (10–15%)
Two sub-areas: data handling and flow (how data is used, flows, and is shared; how input is processed and prompts are built; how proxy filtering and post-processing work) and lifecycle and limitations (visualizing the code-suggestion lifecycle end to end, and the limitations inherent to LLMs and to Copilot specifically).
Where candidates trip:this domain is worth less than the features domain, but the per-question difficulty runs higher, because almost nobody who uses Copilot day to day has ever had a reason to think about the order of operations between input processing, prompt building, and proxy filtering. Familiarity with the feature doesn't transfer to familiarity with its pipeline.
Study tactic: sketch the pipeline yourself — your code and context go in, get processed into a prompt, the model generates a suggestion, it passes through filtering (including the public-code check covered under Domain 6), and only then reaches your editor. Questions about "what happens before X" are really testing whether you can place a stage correctly on that line.
Apply Prompt Engineering and Context Crafting (10–15%)
Two sub-areas: crafting effective prompts (prompt structure and context, how Copilot determines context, zero-shot vs. few-shot prompting, best practices) and engineering prompts for performance (general prompt engineering principles, and how prompt process flow and chat history usage affect results). GitHub's own guidance for Copilot Chat recommends a general-to-specific structure — state the broad goal first, then list specific requirements — and favors giving Copilot concrete examples (sample input/output pairs, reference implementations, unit tests) over abstract instructions.
Where candidates trip: zero-shot and few-shot get reversed under time pressure. Zero-shot means no examples at all — just an instruction. Few-shot means you supply a small number of examples to demonstrate the desired output shape.
Study tactic: this domain rewards hands-on practice more than memorization. Open Copilot Chat, try the same request as a bare instruction and again with one worked example attached, and notice the quality difference yourself — the exam's scenarios mirror that exact contrast.
Improve Developer Productivity with GitHub Copilot (10–15%)
Two sub-areas: enhancing productivity and code quality (code generation, refactoring, documentation, reducing context switching, generating sample data, modernizing legacy code) and supporting testing and security (generating unit and integration tests, identifying edge cases and writing assertions, and suggesting security and performance improvements).
Where candidates trip:treating "Copilot generated the tests" as the end of the task. This domain overlaps with Domain 1's validation requirement — the exam expects you to know that generated tests, refactors, and security suggestions still need human review before they're trusted, not that Copilot replaces that judgment.
Study tactic: for every productivity feature in this domain, pair it mentally with "and then a human verifies X" — that pairing is usually the correct answer when a question offers an option that skips review entirely.
Configure Privacy, Content Exclusions, and Safeguards (10–15%)
Two sub-areas: managing privacy settings and exclusions (configuring content exclusions and editor settings, and describing ownership and limitations of Copilot's outputs) and applying safeguards and troubleshooting (enabling suggestions that match public-code filtering, and resolving issues with suggestions and content exclusions). According to GitHub's documentation on finding public code that matches Copilot suggestions, the choice to allow or block suggestions matching public code is a policy an organization sets — and an enterprise administrator can lock that policy at the enterprise level, overriding whatever an individual organization would otherwise choose.
Where candidates trip: assuming any one setting always wins. The exam tests the policy hierarchy directly — organization-level choices only take effect when the enterprise hasn't already set an explicit policy of its own.
Study tactic: memorize the hierarchy as enterprise overrides organization, not the other way around, and pair it with the fact that content exclusions must be actively configured — they are not a default state you fall back into.
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Start Free Practice →How Much Time Each Domain Deserves
This isn't a calendar — it's a budget. Assuming roughly 18–20 total study hours spread over however many weeks fits your schedule, here's a defensible split based on exam weight adjusted for how unfamiliar each domain tends to be to day-to-day Copilot users:
| Domain | Exam weight | Suggested hours | Why |
|---|---|---|---|
| Use GitHub Copilot features | 25–30% | 6–7 hrs | Largest domain by far — budget the most time here, especially Agent Mode vs. Copilot Edits vs. CLI vs. Coding Agent |
| Use GitHub Copilot responsibly | 15–20% | 3–4 hrs | Small time cost for a high hit rate — six principles are memorizable in one sitting |
| Understand data and architecture | 10–15% | 2–3 hrs | Unfamiliar to most day-to-day users — allocate real time even though the weight looks small |
| Apply prompt engineering and context crafting | 10–15% | 2–3 hrs | Fast to learn if you already use Copilot Chat daily; slower if you don’t |
| Improve developer productivity | 10–15% | 2 hrs | Mostly judgment calls about validating output, not new facts to memorize |
| Configure privacy, content exclusions, and safeguards | 10–15% | 2–3 hrs | Policy-layer questions (org vs. enterprise override) are the sneaky part |
Notice that data-and-architecture and privacy-and-safeguards get more relative time than their raw exam weight would suggest on its own — that's deliberate. Weight tells you how many points a domain is worth; unfamiliarity tells you how many hours it takes to close the gap. Features still gets the largest absolute block because it's worth the most regardless.
MSCertQuiz sells practice-exam access for GH-300 and other Microsoft and GitHub certifications. This guide is written by the same team that builds and maintains those question banks, verified against the official Microsoft Learn study guide and GitHub's own documentation.
Related Resources
Glossary of Copilot terms plus decision tables for feature and plan-tier questions.
One extended scenario per domain, multiple questions against each.
GitHub Foundations — the broad entry-level GitHub certification, a natural pairing.
Browse every Microsoft and GitHub certification MSCertQuiz covers.
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Start Free Practice →Frequently Asked Questions
Is GH-300 a Microsoft or a GitHub certification?
GH-300 (GitHub Copilot) is cataloged directly on Microsoft Learn’s credentials site and scheduled through Pearson VUE, unlike GH-900 (GitHub Foundations), which is administered separately through PSI. GitHub is a Microsoft subsidiary, and this exam sits on Microsoft’s own certification infrastructure.
How many questions are on the GH-300 exam and how long do I get?
GH-300 runs 100 minutes and is commonly reported at around 60 scored questions plus 10–15 unscored pretest items GitHub uses to trial future questions — you can’t tell scored from unscored during the exam, so treat every question as if it counts.
What is the passing score for GH-300?
700 out of 1000, the same scaled threshold used across nearly every Microsoft and GitHub role-based exam. It is not a simple 70% of raw questions correct — scoring is weighted by domain and question difficulty.
Do I need to be a professional developer to pass GH-300?
Microsoft’s official audience profile expects familiarity with GitHub fundamentals and experience with at least one programming language, plus hands-on use of Copilot for productivity, quality, and security — but there are no enforced prerequisites to register.
What changed in the August 2026 GH-300 update?
Microsoft’s change log for the English exam version lists minor updates inside three areas: using Copilot in the IDE, Copilot features and capabilities (Agent Mode, Copilot Edits, MCP, Sub-Agents), and applying safeguards/troubleshooting — no domains were added, removed, or reweighted.
Which domain do most candidates underestimate?
Data and architecture (10–15%). It carries a modest weight, but most day-to-day Copilot users have never had a reason to think about proxy filtering, prompt-building order, or the code-suggestion lifecycle — so the per-question difficulty runs higher than the weight suggests.
How is GH-300 different from GH-900?
GH-900 (GitHub Foundations) is a broad, entry-level tour of Git and GitHub as a platform. GH-300 goes narrow and deep on one product — GitHub Copilot — including its AI architecture, prompt engineering, and organization-wide governance, none of which GH-900 touches.