![[PukiWiki]](image/pukiwiki.png "[PukiWiki]")
「[[マイクロソフト系技術情報 Wiki>http://techinfoofmicrosofttech.osscons.jp/]]」は、「[[Open棟梁Project>https://github.com/OpenTouryoProject/]]」,「[[OSSコンソーシアム .NET開発基盤部会>https://www.osscons.jp/dotNetDevelopmentInfrastructure/]]」によって運営されています。 -[[戻る>GitHub資格Copilot]] --GitHub Copilot - 学習ガイドPDF --[[GitHub Copilot MS Learn Collections]] --[[GitHub Copilot模擬試験]] * 目次 [#w006fb44] #contents *概要 [#af059703] https://assets.ctfassets.net/wfutmusr1t3h/3i7ISEUsTLBgOGrWrML07y/dd586e2b2b607988e2679ed8cce36a76/github-copilot-exam-preparation-study-guide.pdf **Audience Profile [#d6a32beb] This exam is designed for individuals in the field of software development who are proficient in using GitHub, including software developers, administrators, and project managers. This certification is intended for individuals who have a foundational understanding of GitHub Copilot as a product and its available features, along with hands-on experience in optimizing software development workflows using GitHub Copilot. **Objective Domains [#xd3c59e3] An objective domain for a certification exam, often referred to as a “domain” or “exam domain,” is a structured outline or framework that defines the specific knowledge, skills, and topics that the certification exam will cover. It provides a clear roadmap for what candidates should expect to encounter on the exam and what they need to study and prepare for. The domains provided in this study guide are intended to provide insight into the topic categories covered in the GitHub Copilot exam, along with the learning objectives within each domain. |Domain Breakdown|Exam Percentages|h |Domain 1: Responsible AI|7%| |Domain 2: GitHub Copilot plans and features|31%| |Domain 3: How GitHub Copilot works and handles data|15%| |Domain 4: Prompt crafting and Prompt engineering|9%| |Domain 5: Developer use cases for AI|14%| |Domain 6: Testing with GitHub Copilot|9%| |Domain 7: Privacy fundamentals and context exclusions|15%| **Recommendations and Best Practices for Success [#td4e99a7] To increase your chances of success in passing the GitHub Copilot exam, candidates should have a fundamental understanding of GitHub, as well as hands-on experience in using GitHub Copilot features. The recommended learning paths for this exam provide you with an in-depth study of the learning content, followed by hands-on exercises and preparation assessment questions that were created to enable you to fine-tune your knowledge and readiness for the certification exam. *詳細 [#mae1add7] **Content Resources [#f2cc2399] The following resources have been created in collaboration with GitHub as recommended content that covers the learning objectives in each domain for the GitHub Copilot exam. Both Microsoft Learn and LinkedIn Learning provide a complete learning path for the exam, but offer a different learning experience. ***Microsoft Learn [#rbec8ad8] In collaboration with MS Learn, we’ve created two learning paths that offer a comprehensive collection of modules designed to prepare you for using GitHub Copilot effectively. Learn how to enhance your coding experience with AI-powered assistance at every stage of your development lifecycle. GitHub Copilot is an AI tool integrated into GitHub that helps you write code faster and with fewer errors by suggesting code snippets, completing lines, and even generating entire functions. The following modules will guide you through GitHub Copilot's capabilities, equipping you with the skills needed to recognize, apply, and evaluate these features within your own GitHub environment. - GitHub Copilot Fundamentals - Understand the AI Pair Programmer~ https://learn.microsoft.com/en-us/training/paths/copilot/ - Accelerate App Development by Using GitHub Copilot ~ https://learn.microsoft.com/en-us/training/paths/accelerate-app-development-using-github-copilot/ ***LinkedIn Learning [#bb577426] Master the art of coding efficiency and quality with AI-powered assistance throughout your software development process by exploring the Prepare for the GitHub Copilot Certification learning path on LinkedIn Learning. With GitHub Copilot, you have access to a powerful AI-driven tool that helps you write code faster, suggests improvements, and generates entire functions. This video-based learning path features a series of engaging courses that will guide you through the capabilities of GitHub Copilot. By the end of this learning path, you’ll be well-equipped with the knowledge and expertise to seamlessly apply, assess, and maximize GitHub Copilot's features within your coding environment. (Learning path coming soon) **Domain [#m950bc47] ***Domain 1: Responsible AI [#n155439e] -Explain responsible usage of AI --Describe the risks associated with using AI --Explain the limitations of using generative AI tools (dept of the source data for the model, bias in the data, etc.) --Explain the need to validate the output of AI tools --Identify how to operate a responsible AI --Identify the potential harms of generative AI (bias, secure code, fairness, privacy, transparency) --Explain how to mitigate the occurrence of potential harms --Explain ethical AI ***Domain 2: GitHub Copilot plans and features [#mb6946e9] -Identify the different GitHub Copilot plans --Understand the differences between Copilot Individual, Copilot Business, Copilot Enterprise, and Copilot Business for non-GHE --Understand Copilot for non-GitHub customers --Define GitHub Copilot in the IDE --Define GitHub Copilot Chat in the IDE --Describe the different ways to trigger GitHub Copilot (chat, inline chat, suggestions, multiple suggestions, exception handling, CLI) -Identify the main features with GitHub Copilot Individual --Explain the difference between GitHub Copilot Individual and GitHub Copilot Business (data exclusions, IP indemnity, billing, etc. ) --Understand the available features in the IDE for GitHub Copilot Individual -Identify the main features of GitHub Copilot Business --Demonstrate how to exclude specific files from GitHub Copilot --Demonstrate how to establish organization-wide policy management --Describe the purpose of organization audit logs for GitHub Copilot Business --Explain how to search audit log events for GitHub Copilot Business --Explain how to manage GitHub Copilot Business subscriptions via the REST API -Identify the main features with GitHub Copilot Enterprise --Explain the benefits of using GitHub Copilot Chat on GitHub.com --Explain GitHub Copilot pull request summaries --Explain how to configure and use Knowledge Bases within GitHub Copilot Enterprise --Describe the different types of knowledge that can be stores in a Knowledge Base (e.g. code snippets, best practices, design patterns) --Explain the benefits of using Knowledge Bases for code completion and review (e.g. improve code quality, consistency, and efficiency) --Describe instructions for creating, managing, and searching Knowledge Bases within GitHub Copilot Enterprise, including details on indexing and other relevant configuration steps --Explain the benefits of using custom models -Identify the main features with GitHub Copilot Chat --Identify the use cases where GitHub Copilot Chat is most effective --Explain how to improve performance for GitHub Copilot Chat --Identify the limitations of using GitHub Copilot Chat --Identify the available options for using code suggestions from GitHub Copilot Chat --Explain how to share feedback about GitHub Copilot Chat --Identify the common best practices for using GitHub Copilot Chat --Identify the available slash commands when using GitHub Copilot Chat -Using GitHub Copilot in the CLI --Discuss the steps for installing GitHub Copilot in the CLI --Identify the common commands when using GitHub Copilot in the CLI --Identify the multiple settings you can configure within GitHub Copilot in the CLI ***Domain 3: How GitHub Copilot works and handles data [#v418291e] -Describe how GitHub Copilot handles data --Describe how the data in GitHub Copilot individual is used and shared --Explain the data flow for GitHub Copilot code completion --Explain the data flow for GitHub Copilot Chat --Describe the different types of input processing for GitHub Copilot Chat (types of prompts it was designed for -Describe the data pipeline lifecycle of GitHub Copilot code suggestions in the IDE --Visualize the lifecycle of a GitHub Copilot code suggestion --Explain how GitHub Copilot gathers context --Explain how GitHub Copilot builds a prompt --Describe th proxy service and the filters each prompt goes through --Describe how the large language model produces its response --Explain the post-processing of GitHub Copilot’s responses through the proxy server --Identify how GitHub Copilot identifies matching code -Describe the limitations of GitHub Copilot (and LLMs in general) --Describe the effect of most seen examples on the source data --Describe the age of code suggestions (how old and relevant the data is) --Describe the nature of GitHub Copilot providing reasoning and context from a prompt vs calculations --Describe limited context windows ***Domain 4: Prompt crafting and Prompt engineering [#n3146c47] -Describe the fundamentals of prompt crafting --Describe how the context for the prompt is determined --Describe the language options for promoting GitHub Copilot --Describe the different parts of a prompt --Describe the role of prompting --Describe the difference between zero-shot and few-shot prompting --Describe the way chat history is used with GitHub Copilot --Identify prompt crafting best practices when using GitHub Copilot -Describe the fundamentals of prompt engineering --Explain prompt engineering principles, training methods, and best practices --Describe the prompt process flow ***Domain 5: Developer use cases for AI [#i2cc6e7f] -Improve developer productivity --Describe how AI can improve common use cases for developer productivity ---Learning new programming languages and frameworks ---Language translation ---Context switching ---Writing documentation ---Personalized context-aware responses ---Generating sample data ---Modernizing legacy applications ---Debugging code ---Data science ---Code refactoring --Discuss how GitHub Copilot can help with SDLC (Software Development Lifecycle) management --Describe the limitations of using GitHub Copilot --Describe how to use the productivity API to see how GitHub Copilot impacts coding ***Domain 6: Testing with GitHub Copilot [#c24a9e09] -Describe the options for generating testing for your code --Describe how GitHub Copilot can be used to add unit tests, integration tests, and other test types to your code --Explain how GitHub Copilot can assist in identifying edge cases and suggesting tests to address them -Enhance code quality through testing --Describe how to improve the effectiveness of existing tests with GitHub Copilot’s suggestions --Describe how to generate boilerplate code for various tests types using GitHub Copilot --Explain how GitHub Copilot can help write assertions for different testing scenarios -Leverage GitHub Copilot for security and performance --Describe how GitHub Copilot can learn from existing tests to suggest improvements and identify potential issues in the code --Explain how to use GitHub Copilot Enterprise for collaborative code reviews, leveraging security best practices, and performance considerations --Explain how GitHub Copilot can identify potential security vulnerabilities in your code --Describe how GitHub Copilot can suggest code optimizations for improved performance ***Domain 7: Privacy fundamentals and context exclusions [#te50b4b7] -Describe the different SKUs for GitHub Copilot --Describe the different SKUs and the privacy considerations for GitHub Copilot --Describe the different code suggestion configuration options on the organization level --Describe the GitHub Copilot Editor config file -Identify content exclusions --Describe how to configure content exclusions in a repository and organization --Explain the effects of content exlcusions --Explain the limitations of content exclusions --Describe the ownership of GitHub Copilot outputs -Safeguards --Describe the duplication detector filter --Explain contractual protection --Explain how to confiture GitHub Copilot settings on GitHub.com ---Enabling / disabling duplication detection ---Enabling / disabling prompt and suggestion collection --Describe security checks and warnings -Troubleshooting --Explain how to solve the issue if code suggestions are not showing in your editor for some files --Explain why context exclusions may not be applied --Explain how to trigger GitHub Copilot when suggestions are either absent or not ideal --Explain steps for context exclusions in code editors
