Course for GitHub Copilot GH-300 Certification Exam

This course contains the use of artificial intelligence.

Additional Material

• Study Material [Download eBook in PDF}: Please download the PDF book (230 pages, study guide) as a companion resource for your certification exam preparation. The download link is provided in the Resources section of Practice Paper 1, Question 1. It is must you go through this book.

Note: GitHub® and GitHub Copilot are registered trademarks of GitHub, Inc. This course material is not affiliated with or endorsed by GitHub, Inc.

Pass the GitHub Copilot Certification (GH-300) with a laser-focused, exam-ready program designed for real developers and teams. This course gives you everything you need to master the GH-300 blueprint—Responsible AI, Copilot plans and features, how Copilot works and handles data, prompt crafting/engineering, developer productivity use cases, testing, privacy fundamentals, and context exclusions—so you can walk into the exam confident and walk out certified. You’ll practice with official-style questions, hands-on prompt labs, and step-by-step workflows in VS Code or JetBrains, learning exactly how to use Copilot Chat, inline suggestions, and the CLI to accelerate day-to-day coding while staying compliant with enterprise policies.

Why this course stands out for GH-300:
• 100% certification preparation: domain-by-domain coverage mapped to the stated exam weighting.
• Practice-first design: exam-style questions with detailed explanations and rationales.
• Real workflows: prompt-to-PR scenarios, code review summaries, and test generation in modern stacks.
• Enterprise readiness: org policies, audit logs, content exclusions, duplication detection, and privacy settings explained with clear, reproducible steps.
• Responsible AI throughout: limitations, bias, secure coding, data handling, and validation strategies woven into every module.

What you’ll master (aligned to GH-300 domains):
• Responsible AI (7%): risks, limitations, validation, fairness, privacy, and secure-by-design practices you’ll be tested on.
• Plans & features (31%): Copilot Individual vs Business vs Enterprise, billing & data exclusions, Copilot Chat in IDE and on GitHub, multiple suggestion modes, exception handling, CLI, org-wide policy management, and audit log queries.
• How Copilot works & handles data (15%): context gathering, prompt construction, proxy filters, model response lifecycle, post-processing, matching code detection, and data flows for code completion vs Chat.
• Prompt crafting & engineering (9%): zero-shot vs few-shot, structuring prompts, chat history usage, context boundaries, and durable best practices.
• Developer use cases (14%): refactoring, debugging, documentation, sample data, legacy modernization, productivity metrics, and SDLC acceleration with measurable ROI.
• Testing with Copilot (9%): generating unit/integration tests, identifying edge cases, strengthening assertions, and editor configuration for higher-quality suggestions.
• Privacy & context exclusions (15%): repository/org exclusions, scope/limits, ownership of outputs, duplication detector, contractual protections, security checks, and troubleshooting.

Hands-on, practical learning:
• IDE setup quick start for VS Code/JetBrains/Neovim with Copilot Chat and inline assistance.
• Prompt labs: translate requirements into precise prompts; compare zero-shot vs few-shot; iterate for higher-quality completions.
• Testing drills: generate test suites, cover edge cases, and refine assertions to improve reliability.
• Enterprise controls: enable/disable duplication detection, configure content exclusions, and explore audit events to meet compliance.
• Troubleshooting guide: resolve “no suggestions,” fix sub-optimal context, and trigger Copilot effectively across editors.

Who this is for:
• Software developers, QA/test automation engineers, DevOps/SRE, data engineers, solution architects, tech leads, and engineering managers evaluating Copilot for teams.
• GitHub admins/program managers responsible for Business/Enterprise rollout, policy controls, and auditability.
• Anyone targeting GH-300 and wanting a clear, structured path from fundamentals to exam-ready proficiency.

What’s included:
• Domain-mapped lessons that mirror the GH-300 outline.
• Exam-style practice with full explanations and references for every answer.
• Downloadable checklists and cheat sheets: prompt engineering, privacy & exclusions, enterprise setup, and troubleshooting.
• Realistic scenarios that translate directly to daily work—prompt-to-PR, code review summaries, documentation generation, and metric tracking for Copilot impact.

Outcomes you can prove:
• Confidently compare Copilot plans and deploy the right SKU with the right policy controls.
• Explain and diagram how Copilot gathers context and produces responses—exactly what GH-300 tests.
• Apply prompt engineering to boost suggestion quality, reduce retries, and ship faster with fewer defects.
• Generate robust tests, catch edge cases early, and improve code health with Copilot-assisted assertions.
• Enforce privacy safeguards, content exclusions, and duplication detection to protect IP and reduce risk.
• Use productivity metrics to quantify Copilot ROI and communicate value to stakeholders.

Prerequisites:
No mandatory prerequisites. A GitHub account, an IDE (VS Code or JetBrains), and comfort with one programming language (Python, JavaScript/TypeScript, Java, C#, or Go) will help you get the most from the course.

If your goal is to pass GH-300 on the first attempt and adopt GitHub Copilot responsibly and effectively, this exam-ready course gives you the structure, practice, and confidence to succeed. Enroll now and turn AI-assisted development into a measurable advantage—on the exam and on the job.