由頂級 GitHub 專家團隊以最高技術水平整理製作，確保 GitHub-Copilot 題庫的專業性和準確性。

本題庫源自我們的 GitHub 專家對目前最新的 GitHub-Copilot 輔導指南的整編，可以對您的學習起到極大的幫助作用。我們的團隊成員都是來自指定認證專家、培訓師和 GitHub 相關工作從業者，他們對 GitHub-Copilot 考試內容和 GitHub 認證要求的資歷瞭如指掌，這樣可以確保 GitHub-Copilot 練習題庫的高質量。你只需在參加 GitHub-Copilot 考試前认真學習下本題庫，將有助於順利完成考試。

GitHub GitHub-Copilot 考試概覽:

認證廠商:	GitHub (Microsoft)
考試名稱:	GitHub Copilot 認證考試
考試代碼:	GH-300
支援語言:	English, Spanish, Portuguese (Brazil), Korean, Japanese
及格分數:	官方未正式公開（通常回報約 700/1000）
考試形式:	多選題, 情境式題目, 監考考試（Pearson VUE）
考試時間:	100 minutes
考試費用:	$99 美元
相關認證:	GitHub 進階安全認證
證照有效期限:	24 個月
實際考試題數:	約 60 道計分題目 + 未計分項目
推薦課程:	GitHub 文件 Copilot 指南 GitHub Copilot 學習路徑（Microsoft Learn）
考試報名:	Microsoft Learn 認證官方 GitHub Copilot 認證頁面
範例考題:	GitHub GitHub-Copilot 範例考題
考試方式:	線上監考或 Pearson VUE 測試中心
必備條件:	無正式先決條件；建議具備 GitHub 和基本程式設計經驗
官方大綱網址:	https://learn.github.com/certification/COPILOT

GitHub GitHub-Copilot 考試大綱：

主題	簡介
主題 1	Testing with GitHub Copilot: This section of the exam measures skills of QA Engineers and Test Automation Specialists and covers AI-assisted testing methodologies, including the generation of unit tests, integration tests, and edge case detection. It explains how GitHub Copilot improves test effectiveness by suggesting relevant assertions and boilerplate test cases. The section also discusses privacy considerations, organizational code suggestion settings, and best practices for configuring GitHub Copilot’s testing features.
主題 2	Prompt Engineering: This section of the exam measures skills of AI Engineers and Software Developers and covers the fundamentals of prompt engineering, including key principles, techniques, and best practices for generating high-quality outputs. It explains different prompting strategies such as zero-shot and few-shot prompting, how context influences AI-generated responses, and the role of structured prompts in guiding Copilot's behavior. It also discusses the prompt lifecycle and ways to enhance model performance through refined input instructions.
主題 3	How GitHub Copilot Works and Handles Data: This section of the exam measures the skills of Data Security Specialists and DevOps Engineers and covers how GitHub Copilot processes data, handles code suggestions and manages privacy concerns. It explains the data pipeline for Copilot’s suggestions, how it gathers context, and how prompts are processed through its AI model. The section also discusses the limitations of AI-generated code, the effects of historical data on suggestions, and the role of prompt crafting. Best practices for improving prompt effectiveness and optimizing AI-generated responses are included.
主題 4	Developer Use Cases for AI: This section of the exam measures skills of Full-Stack Developers and Cloud Engineers and covers how AI enhances developer productivity across various tasks such as learning new programming languages, debugging, writing documentation, and refactoring code. It discusses how GitHub Copilot integrates with the Software Development Lifecycle (SDLC) and its role in modernizing legacy applications. It also highlights the use of AI for personalized responses, sample data generation, and improving overall efficiency in software development.
主題 5	Responsible AI: This section of the exam measures the skills of AI Ethics Analysts and AI Developers and covers the principles of responsible AI usage, the risks associated with AI, and the limitations of generative AI tools. It includes the importance of validating AI-generated outputs and operating AI systems responsibly. It also explores potential harms such as bias, privacy concerns, and fairness issues, along with methods to mitigate these risks. The ethical considerations of AI development and deployment are also discussed.
主題 6	Privacy Fundamentals and Context Exclusions: This section of the exam measures skills of Cybersecurity Specialists and Compliance Officers and covers privacy safeguards and content exclusion settings in GitHub Copilot. It explains how Copilot can identify security vulnerabilities, suggest optimizations, and enforce secure coding practices. It also includes details on content ownership, data filtering mechanisms, and exclusion configurations. The section concludes with troubleshooting guidelines for managing context exclusions and ensuring compliance with organizational security policies.