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

How I passed the GH-300 (GitHub copilot) certification ## Table of Contents ## What is GH-300? ## Exam basics (quick facts) ## Assessment ## Main course I used (core resource) ## Why this worked for me ## What I learned the most ## Principles of prompt engineering ## The 4 Ss of prompt engineering ## Practical example ## How Copilot learns from our prompts ## How GitHub copilot processes our input ## Extra Microsoft learn modules I found useful ## GitHub and advanced topics I explored ## Practice questions ## My Final Tips ## Final Thoughts 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. 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. More details provided by Microsoft on their exam study guide. Check the official: Microsoft course GH-300T00-A: GitHub Copilot What you need to know before the exam: You can check your knowledge and try the official practical exam which is free and a great way to identify your weak areas. I relied primarily on the Microsoft Learn GH-300 course: Since I prefer reading over videos, this format let me focus on what matters. This alone improves Copilot output dramatically. Zero-shot learning The model generates an answer based only on your prompt, without any examples. One-shot learning The model is given one example along with the prompt, helping it understand the desired format or logic. 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. Understanding this helps with both exam questions and real usage: 👉 If you want to see my detailed study notes you can check it via Notion These are not strictly required for GH-300, but they clarify concepts: These helped me understand real-world usage beyond the exam: How Copilot handles data Official Copilot docs Spec-driven development with GitHub Spec Kit Code reviews & PR issues with Copilot MCP Server setup in VS Code 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: GH-300 is not about being a "Copilot power user." It's about: Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK: 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 CODE_BLOCK: BAD: "Write a function" GOOD: "Write a TypeScript function that validates email addresses using regex. Include error handling and return a Boolean." CODE_BLOCK: BAD: "Write a function" GOOD: "Write a TypeScript function that validates email addresses using regex. Include error handling and return a Boolean." - What is GH-300? - Exam basics (quick Facts) - Main course I used (core resource) - What I learned the most Principles of prompt engineering How Copilot learns from our prompts How GitHub copilot processes our input - Principles of prompt engineering - How Copilot learns from our prompts - How GitHub copilot processes our input - Extra Microsoft learn modules I found useful - GitHub and advanced topics I explored - Practice questions - My final tips - Final thoughts - Principles of prompt engineering - How Copilot learns from our prompts - How GitHub copilot processes our input - 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%) - 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 - Text-based content - Easy to skim and navigate - Clear learning objectives - No unnecessary fluff - 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 - Zero-shot learning The model generates an answer based only on your prompt, without any examples. - One-shot learning The model is given one example along with the prompt, helping it understand the desired format or logic. - 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. - Secure prompt transmission Your prompt is securely sent to GitHub servers. - Your prompt is securely sent to GitHub servers. - Proxy filtering Blocks prompt injection attacks Prevents system manipulation - Blocks prompt injection attacks - Prevents system manipulation - Toxicity filtering Blocks hate speech Filters inappropriate content Redacts detected personal data - Blocks hate speech - Filters inappropriate content - Redacts detected personal data - Code generation using LLMs Models trained on public code Generates based on context and prompts - Models trained on public code - Generates based on context and prompts - Your prompt is securely sent to GitHub servers. - Blocks prompt injection attacks - Prevents system manipulation - Blocks hate speech - Filters inappropriate content - Redacts detected personal data - Models trained on public code - Generates based on context and prompts - Introduction to GitHub Copilot Enterprise - Copilot for Business - Accelerate app development using Copilot - Introduction to Vibe Coding - How Copilot handles data - Official Copilot docs - Spec-driven development with GitHub Spec Kit - Code reviews & PR issues with Copilot - MCP Server setup in VS Code - 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 - 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 - Skip the "boring" sections on ethics and privacy - Memorize answers without understanding reasoning - Ignore the official study guide - 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