How I prepared for the GitHub Copilot GH-300 exam: 5 steps

Over the last two weeks, I spent some of my free time preparing for the GitHub Copilot certification exam. In this blog post I will share with you how I prepared for it in 5 steps.

First - what is GitHub Copilot and why is this certification important?

GitHub Copilot is an AI assistant that comes to your aid either by brainstorming code ideas, providing inline code completions, or fully and autonomously generating code for you.

You are able to use it:

• within your IDE as chat and completion extensions;
• via the terminal if you decide to go with GitHub Copilot CLI (which is a terminal-based agent);
• with the desktop app or in the cloud;
• or on GitHub's website for your repos, actions and pull requests.

Behind it stands a powerful LLM - the brain - with GitHub Copilot as the hands. You will learn more in the GH-300 study guide that I used for myself and which I will present to you below.

Why is this certification important?

It proves that you can work effectively with the tool and, therefore, you can attract the attention of companies working with GitHub and GitHub Copilot. In the era we live in, each certification is welcome. And the value is even bigger when the certification is official - earned by paying for and passing a proctored exam. Training materials are free.

Which 5 steps did I use to prepare for the GitHub Copilot GH-300 exam?

Step 1 - ensure you read all the learning materials first!

Your journey for the GH-300 exam preparation should begin with the official certification page in Microsoft Learn. Here you will find all the available information about the exam, useful links regarding question types (there is even a sandbox environment to get a better feel for the different question types), rules and, of course, the examination price. There are other useful things to read here, so treat this as your first and official starting point. And you will notice the section titled (at least by the time this blog post is written) "Prepare for the exam". The content in this section leads us to:

Step 2 - ensure you passed the two learning paths with all their modules and module assessments!

As the step title suggests - there are two learning paths linked in the section on the certification page which are crucial if you are considering taking the GitHub Copilot certification exam. Both are mandatory in my opinion. Read all modules, take notes if this helps you and make sure you pass the module assessment after each module. Each module ends with a summary page containing useful links. I personally decided to re-take some of the assessments several times after re-visiting some of the topics during my preparation. This helps you remember important details that might be part of the actual exam.

Step 3 - ensure you passed the sample exam!

While I was preparing, the official practice assessment was (at that time) part of the certification page, and I was lucky enough to be able to take and pass it. I wanted to re-visit it later but the page got updated and it suggested going to the AI Skills Navigator GitHub Copilot certification page as the practice assessment had moved there (link for that is on the Microsoft Learn page). And indeed at the time of my preparation there was a button dedicated to starting the practice assessment, but for some reason it was not working. Later that button was gone. So I cannot share what that assessment looked and felt like, but hopefully for your preparation it will be live and operational again. If it is, taking it is a must, but only after passing Step 2!

Step 4 - build your own AI-based practice exam simulator (optional)

Here I used Claude Code with an Ollama cloud hosted model (GLM-5.3-flash to be more precise). I needed an HTML-based exam simulator that will make me feel as close as possible to a real examination process. In practice, the HTML had to serve questions of different types, score them with weighted metrics (more on that in a moment) and - last but not least - link to the official documentation for every wrong answer. I asked Claude Code to shift questions several times with the highest possible difficulty it could think of and took all exams. I learned what I got wrong, read the official materials and documentation and took notes.

With AI comes the risk of hallucinations. You will be reading about this during Step 2, of course. But it is something we should keep constantly reminding ourselves about. That is why, to minimize the risk of wrong practice questions, wrong answers or wrong evaluation, I asked several sub-agents acting as senior SDETs / senior QA engineers to verify everything based on code functionality, evaluation logic and data from the official public URLs only.

As for the evaluation logic - the one for the real exam is proprietary and strictly confidential. However, in the requirements for passing, it is stated that a result of "700/1000" is the bare minimum of passing the GitHub Copilot exam (sources on these numbers: the official GH-300 study guide and Exam scoring and score reports). So the model created similar logic that resembles such evaluation, but of course there should not be any expectations that this is even close to the official one. Still, it gives you a good feel for the practice experience.

I will not share the code's final output or any of its questions, because I cannot 100% guarantee that every item is AI-generated practice material rather than content recalled from an unofficial site. But I will share the prompt that I used (refined with GLM-5.3-flash based on my edits - a single, ready-to-paste version) so that you can try it on your end if you want to take this step.

Another thing worth mentioning is that I asked the model to refine the prompt so that even if you provide any questions, they are not taken into consideration for the practice exam generation. This should keep you on the safe side in case you decide to share your end result or modified prompt with someone. I suggest you keep that instruction.

So here is my prompt - please feel free to refine it as you see fit.

ROLE
You are acting as a staff developer. Build me an OFFLINE practice-exam simulator
for the Microsoft/GitHub "GH-300: GitHub Copilot" certification, as a single
plain HTML/CSS/vanilla-JS project in this folder (no frameworks, no bundlers,
opened directly via file:// by double-clicking index.html).

STEP 1 - RESEARCH (official sources only)
Use ONLY these official pages; do not use any third-party exam-prep content,
question dumps, or user-supplied question texts:
- Certification overview & exam facts:
  https://learn.microsoft.com/en-us/credentials/certifications/github-copilot
- Official study guide (skills measured, weights, change log):
  https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/GH-300
- Exam duration/question-count/experience and scoring policies:
  https://learn.microsoft.com/en-us/credentials/support/exam-duration-exam-experience
  https://learn.microsoft.com/en-us/credentials/certifications/exam-scoring-reports
- Official learning paths: GitHub Copilot Fundamentals Part 1 and Part 2
  (study guide links them; use every module of both)
- GitHub Copilot product docs as needed:
  https://docs.github.com/copilot (plans, customization, content exclusions,
  Copilot Chat, CLI, coding agent, code review, privacy/Trust Center)
- VS Code AI documentation (inline suggestions, chat, agent mode):
  https://code.visualstudio.com/docs/agents/overview
For every question you invent, record the ONE official page that supports it.

STEP 2 - EXAM MODEL (mirror the official facts you found)
- 100 minutes, ~40–60 questions as publicly reported, scaled score of 1000, pass mark 700.
- Skill-area blueprint with official weights: 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%).
- Question types in exam style: single choice; multiple-select ("select all
  that apply", all-or-nothing exact set); Yes/No statement chains (partial
  credit per statement); drag-to-match; build-ordered-list; hot-area.
- Pearson VUE–like flow: candidate-agreement screen → timed exam with
  countdown → mark-for-review flags → question navigator → review screen →
  finish confirmation → score report.

STEP 3 - ENGINE + QUESTION BANK
- A question bank file holding ORIGINAL questions ONLY, written by you from
  the official pages above. For every question: id, skill area, type,
  options, the correct key, an explanation, and the official source URL.
  Version-marker each question (v1, v2, …) with an "active" flag, so a
  rotation can retire old sets and append new ones and every future attempt
  re-randomizes and draws new questions blueprint-weighted.
- Shuffle options/pairs/steps per attempt; derive correct keys from content,
  never from display order; keep scoring exact (all-or-nothing except
  Yes/No chains).

STEP 4 - EVALUATION
- Scaled score + pass/fail, per-skill-area accuracy table, per-question
  review (my answer vs correct, with explanation + official reference),
  downloadable text report, and attempt history in localStorage.

STEP 5 - SELF-VERIFICATION BEFORE HAND-OVER (act as your own SDET)
- Machine-check with node: unique ids; every correct key in range; simulate
  the scoring so correct answers score exactly 1 and blank/partial answers
  score 0 or fractions (beware empty-list edge cases); simulate 300 random
  attempts against the blueprint; verify every explanation/source pair by
  fetching the cited official page and confirming the answer key against it.
- Fix anything found, re-verify, and report the results when handing over.

SAFETY & LEGAL CONSTRAINTS (non-negotiable)
- Never include, paraphrase, or imitate actual confidential exam questions;
  generate original practice items from the official learning material only.
- No logos, brand artwork, or trademarked graphics in the code or UI;
  descriptive text references (e.g., "GH-300") are fine.
- Include a visible disclaimer on the start screen: unofficial training
  simulator, not affiliated with or endorsed by Microsoft or GitHub,
  questions are AI-generated practice material - always verify against the
  official documentation.
- Prefer facts already verified from official pages; if a fact is
  version-sensitive (features evolve fast), phrase it generically or cite
  the changelog that documents the change.

DELIVERABLE
The complete folder (index.html, css, engine, configurable exam bank +
config, a README with the rotation instructions), your SDET verification
report, and a list of the official pages you relied on.

Step 5 - search for sample practice questions online and/or ask other chatbots for quick random trainings (optional)

Explore the public online learning materials using your favorite search engine or ask AI for assistance. Aim for credible sources that provide practice questions and answers. And no matter what information you discover - treat it with a healthy dose of doubt and always verify it with the official docs or learning materials! Remember that the learning paths, the public GitHub Copilot documentation and the official Microsoft Learn certification page for the exam (with everything linked from it) are your only 100% credible sources of truth!

As for other quick and on-the-go trainings generated by other AI chatbots - treat this suggestion as a quick check on several questions to keep your brain occupied with the topic in your free time. Again remember that any AI can hallucinate, so always check the results against the official pages.

That is how I was able to achieve my certification - I hope it helps you too. Good luck!

The featured image is AI-generated, with the tools stated in the blog's terms.