Prepare for GitHub Copilot GH-300 with 75 original, explained practice questions in two timed tests. This course is for developers, team leads, and administrators who use Copilot to improve software delivery while maintaining sound review and privacy practices. The questions are scenario based: you choose an appropriate feature, improve a prompt, identify a data or security risk, or decide how to validate generated work.  The bank follows the current official GH-300 study topics. Responsible AI scenarios cover incorrect or biased output, sensitive data, provenance concerns, code review, and human accountability. Product questions explore inline suggestions, Copilot Chat, CLI, agent mode, Copilot Edits, code review, pull request summaries, Spaces, Spark, reusable instructions, and organization policies. Data and architecture questions ask you to reason about context, prompt construction, suggestion processing, and model limitations.  Prompt engineering questions compare vague and precise requests, zero-shot and few-shot examples, task constraints, context selection, and iterative feedback. Productivity questions cover tests, edge cases, documentation, refactoring, modernization, and measured performance work. Privacy questions address content exclusions, client support differences, public code matching, data minimization, and audit evidence. Where feature behavior varies by client or plan, the questions emphasize checking current documentation rather than assuming universal support.  Each question has an answer explanation. The two tests contain 38 and 37 questions, are 45 minutes each, and use an 80% editorial practice target. That target is not the official certification passing score. Take a test without notes, review every explanation, then revisit weak areas using current GitHub documentation. These are independently authored learning questions, not actual or recalled exam content. This course is not endorsed by GitHub or Microsoft, and no certification outcome is guaranteed.