DEV Community

Cover image for A reading first GH-300 cert prep: what to study, what to skip, and what actually matters
Majdi Zlitni
Majdi Zlitni

Posted on

A reading first GH-300 cert prep: what to study, what to skip, and what actually matters

#githubcopilot #certification #learning #github

How I passed the GH-300 (GitHub copilot) certification

In this blog I will explains how I prepared for the GH-300 GitHub Copilot certification, what resources actually mattered, and what I learned especially if you prefer reading over watching videos, like I do.

Table of Contents

What is GH-300?

GH-300 is a Microsoft certification exam focused on GitHub Copilot.

It validates the ability to use GitHub Copilot effectively and responsibly in real-world development workflows.

Key topics include:

  • Responsible AI (7%)
  • GitHub copilot plans and features (31%)
  • How GitHub copilot works and handles data (15%)
  • Prompt crafting and prompt engineering (9%)
  • Testing with GitHub copilot (9%)
  • Privacy fundamentals and context exclusions (15%)

More details provided by Microsoft on their exam study guide.

Check the official: Microsoft course GH-300T00-A: GitHub Copilot

Exam basics (quick facts)

What you need to know before the exam:

  • Difficulty: Generally easy, especially if you're familiar with GitHub Copilot in your IDE
  • Exam code: GH-300
  • Duration: 1h40 minutes
  • Questions: ~60–65
  • Format: Multiple choice, multiple answer, scenarios

Assessment

You can check your knowledge and try the official practical exam which is free and a great way to identify your weak areas.

Main course I used (core resource)

I relied primarily on the Microsoft Learn GH-300 course:

Why this worked for me

  • Text-based content
  • Easy to skim and navigate
  • Clear learning objectives
  • No unnecessary fluff

Since I prefer reading over videos, this format let me focus on what matters.

What I learned the most

Principles of prompt engineering

The 4 Ss of prompt engineering

  • Single → One well-defined task
  • Specific → Clear and explicit instructions
  • Short → Concise, straight to the point
  • Surround → Use descriptive filenames & keep related files open for context

This alone improves Copilot output dramatically.

Practical example 

BAD: "Write a function"

GOOD: "Write a TypeScript function that validates email addresses using regex. Include error handling and return a Boolean."
Enter fullscreen mode Exit fullscreen mode

How Copilot learns from our prompts

  • Zero-shot learning
    The model generates an answer based only on your prompt, without any examples.

    Zero-shot learning

  • One-shot learning
    The model is given one example along with the prompt, helping it understand the desired format or logic.
    One-shot learning

  • Few-shot learning
    The model is provided with several examples in the prompt, allowing it to better generalize and produce more accurate or relevant outputs.
    Few-shot learning

How GitHub copilot processes our input

Understanding this helps with both exam questions and real usage:

Data processing

  1. Secure prompt transmission
    • Your prompt is securely sent to GitHub servers.
  2. Proxy filtering
    • Blocks prompt injection attacks
    • Prevents system manipulation
  3. Toxicity filtering
    • Blocks hate speech
    • Filters inappropriate content
    • Redacts detected personal data
  4. Code generation using LLMs
    • Models trained on public code
    • Generates based on context and prompts

👉 If you want to see my detailed study notes you can check it via Notion

Extra Microsoft learn modules I found useful

These are not strictly required for GH-300, but they clarify concepts:

GitHub and advanced topics I explored

These helped me understand real-world usage beyond the exam:

Practice questions

This open source repo is a life saving because it contain really good practical questions with explanations and hints directing to official sources you can check it practical copilot exam:

ghcertified

  • Exam-style practice questions
  • Detailed explanations for each answer
  • Links directing to official Microsoft Learn resources
  • Not a question dump it's a legitimate study project to help people pass GitHub certifications

My Final Tips

DO:

  • Start with practice assessments to identify gaps
  • Use Microsoft Learn, it's the source of truth
  • Focus on concepts and reasoning, not memorization
  • Understand why Copilot behaves the way it does
  • Study Responsible AI thoroughly it's heavily weighted
  • Practice prompt engineering with real examples
  • Review enterprise features

DON'T:

  • Skip the "boring" sections on ethics and privacy
  • Memorize answers without understanding reasoning
  • Ignore the official study guide

Final Thoughts

GH-300 is not about being a "Copilot power user." It's about:

  • Understanding how Copilot works data flow, security, limitations
  • Using it responsibly ethics, bias awareness, copyright considerations
  • Integrating it effectively real developer workflows, best practices
  • Knowing when to trust (and distrust) AI-generated code

Good luck!

Top comments (0)