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| Topic | Details |
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| Topic 1 | - 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.
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| Topic 2 | - 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.
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| Topic 3 | - 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.
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| Topic 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.
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GitHub CopilotCertification Exam Sample Questions (Q71-Q76):
NEW QUESTION # 71
What is the impact of the "Fill-In-the-Middle" (FIM) technique on GitHub Copilot's code suggestions?
- A. Ignores both the prefix and suffix of the code, focusing only on user comments for context.
- B. Improves suggestions by considering both the prefix and suffix of the code, filling in the middle part more accurately.
- C. Allows Copilot to generate suggestions based only on the prefix of the code.
- D. Restricts Copilot to use only external databases for generating code suggestions.
Answer: B
Explanation:
The Fill-In-the-Middle technique lets Copilot look at both what comes before and after your cursor, so it can generate the "missing" code between those segments more accurately.
NEW QUESTION # 72
Select a strategy to increase the performance of GitHub Copilot Chat.
- A. Optimize the usage of memory-intensive operations within generated code
- B. Limit the number of concurrent users accessing GitHub Copilot Chat
- C. Apply prompt engineering techniques to be more specific
- D. Use a single GitHub Copilot Chat query to find resolutions for the collection of technical requirements
Answer: C
Explanation:
Applying prompt engineering techniques to be more specific is the best strategy to increase the performance and relevance of GitHub Copilot Chat's responses.
NEW QUESTION # 73
How does GitHub Copilot Enterprise assist in code reviews during the pull request process?
(Select two.)
- A. It can validate the accuracy of the changes in the pull request.
- B. It can answer questions about the changeset of the pull request.
- C. It automatically merges pull requests after an automated review.
- D. It generates a prose summary and a bulleted list of key changes for pull requests.
Answer: B,D
Explanation:
GitHub Copilot Enterprise assists in code reviews by generating summaries of pull requests and answering questions about the changes made.
NEW QUESTION # 74
How does GitHub Copilot Enterprise assist in code reviews during the pull request process? (Select two.)
- A. It can validate the accuracy of the changes in the pull request.
- B. It can answer questions about the changeset of the pull request.
- C. It automatically merges pull requests after an automated review.
- D. It generates a prose summary and a bulleted list of key changes for pull requests.
Answer: B,D
Explanation:
GitHub Copilot Enterprise assists in code reviews by generating summaries of pull requests and answering questions about the changes made.
NEW QUESTION # 75
Which Copilot Individual features are available when using a supported extension for Visual Studio, VS Code, or JetBrains IDEs? (Each correct answer presents part of the solution. Choose two.)
- A. Chat
- B. Knowledge Base
- C. Pull Request Diff Analysis
- D. Code suggestions
Answer: A,D
Explanation:
GitHub Copilot Individual provides code suggestions and chat features when used with supported IDE extensions like Visual Studio, VS Code, and JetBrains IDEs.
NEW QUESTION # 76
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