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正確的GitHub-Copilot考試證照綜述和資格考試考試材料領導者和最好的GitHub-Copilot權威認證

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KaoGuTi的最新的GitHub GitHub-Copilot 認證考試練習題及答案問世之後，通過GitHub GitHub-Copilot 認證考試已經不再是IT職員的夢想了。KaoGuTi提供的所有關於GitHub GitHub-Copilot 認證考試練習題及答案品質都是是很高的，和真實的考試題目有95%的相似性。KaoGuTi是值得你擁有的。如果你選擇了KaoGuTi的產品，你就為GitHub GitHub-Copilot 認證考試做好了充分準備，成功通過考試就是很輕鬆的。

>> GitHub-Copilot考試證照綜述 <<

GitHub-Copilot考試證照綜述＆有保障的GitHub GitHub-Copilot考試成功 - 更新的GitHub-Copilot權威認證

作為一位 GitHub GitHub-Copilot 考生而言，作好充分的準備可以幫助您通過考試。首先您必須去當地考試中心咨詢相關考試信息，然后挑選最新的 GitHub-Copilot 考試題庫，因為擁有了最新的 GitHub-Copilot 考試題庫可以有利的提高通過考試的機率。使用KaoGuTi 的題庫可以節省您寶貴的時間，保證你順利通過 GitHub-Copilot 考試。既能幫您節省時間，又可以順利幫助您通過考試，這將是您的最佳選擇。

GitHub GitHub-Copilot 考試大綱：

主題	簡介
主題 1	GitHub Copilot Plans and FeaturesThis section of the exam measures the skills of Software Engineers and IT Administrators and covers different GitHub Copilot plans, including Individual, Business, and Enterprise editions. It explains the integration of GitHub Copilot within IDEs and discusses key features such as inline chat, multiple suggestions, and exception handling. The section details the policies for managing GitHub Copilot within organizations, including auditing logs and API management. It also highlights advanced functionalities like knowledge bases for improved code quality and best practices for Copilot Chat usage.
主題 2	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 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	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 5	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 6	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.

 

最新的 GitHub Certification GitHub-Copilot 免費考試真題 (Q44-Q49):

問題 #44
What role does chat history play in GitHub Copilot's code suggestions?

• A. Chat history is used to train the GitHub Copilot model in real-time.
• B. Chat history provides context to GitHub Copilot, improving the relevance and accuracy of its code suggestions.
• C. Chat history is irrelevant to GitHub Copilot and does not affect its functionality.
• D. Chat history is stored and shared with other users to enhance collaboration.

答案：B

解題說明：
Chat history provides valuable context to GitHub Copilot, helping it generate more relevant and accurate code suggestions based on previous interactions and conversations.

 

問題 #45
What is the correct way to exclude specific files from being used by GitHub Copilot Business during code suggestions?

• A. Use the GitHub Copilot settings in the user interface to exclude files.
• B. Add the specific files to a copilot.ignore file.
• C. Modify the .gitignore file to include the specific files.
• D. Rename the files to include the suffix _no_copilot.

答案：A

解題說明：
The correct way to exclude files is through the GitHub Copilot settings in the user interface, which allows administrators to specify files and directories to be ignored.

 

問題 #46
How does the /tests slash command assist developers?

• A. Constructs detailed test documentation.
• B. Integrates with external testing frameworks.
• C. Executes test cases to find issues with the code.
• D. Creates unit tests for the selected code.

答案：D

解題說明：
The /tests slash command in GitHub Copilot Chat creates unit tests for the selected code, helping developers ensure the functionality and reliability of their code.

 

問題 #47
What practices enhance the quality of suggestions provided by GitHub Copilot? (Select three.)

• A. Including personal information in the code comments
• B. Clearly defining the problem or task
• C. Use a .gitignore file to exclude irrelevant files
• D. Providing examples of desired output
• E. Using meaningful variable names

答案：B,D,E

解題說明：
The quality of Copilot's suggestions is enhanced by clearly defining the task, using meaningful variable names, and providing examples of the desired output.

 

問題 #48
Which of the following is correct about GitHub Copilot Knowledge Bases?

• A. It is an Enterprise feature
• B. All repos are indexed
• C. All file types are indexed
• D. Indexing is static

答案：A

解題說明：
GitHub Copilot Knowledge Bases is an Enterprise feature that allows the tool to use an organization's internal documentation and code for more accurate and relevant suggestions.

 

問題 #49
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