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| Section | Weight | Objectives |
|---|---|---|
| Privacy Fundamentals and Context Exclusions | 15% | - Content exclusions at repository and organization level - Effects and limitations of content exclusions - Safeguards: duplication detector, contractual protection, data collection settings - Troubleshooting: missing suggestions, exclusions not applied, triggering Copilot - Ownership of AI-generated outputs |
| How GitHub Copilot Works and Handles Data | 15% | - Privacy and data handling within Copilot - Understanding that private repository content is not used to train models - AI model response generation - The prompt creation process - How Copilot gathers code context |
| Responsible AI with GitHub Copilot | 7% | - Bias and fairness in AI-generated code - Privacy and transparency considerations - Risks and limitations of generative AI tools - Validating AI output and mitigation strategies - Ethical AI principles and responsible use |
| Prompt Engineering and AI Interaction | 9% | - Improving Copilot responses using context - Refining prompts for better coding results - Creating effective prompts - Crafting and refining prompts (prompt engineering) |
| GitHub Copilot Plans and Features | 31% | - Configuration options and productivity features - Differences between Copilot Individual, Business, and Enterprise plans - Implementing and managing Copilot plans for an organization - Copilot Chat and code suggestion capabilities - IDE integration (VS Code, Visual Studio, JetBrains) |
| Developer Use Cases for AI | 14% | - Automating repetitive development tasks - Documentation generation - Code generation and refactoring - Learning new languages and frameworks - Debugging applications - SDLC support and productivity insights |
| Testing with GitHub Copilot | 9% | - Identifying edge cases - Editor configuration and org-level suggestion settings - Improving test coverage with AI suggestions - Generating unit tests and integration tests |
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NEW QUESTION # 41
How long does GitHub retain Copilot data for Business and Enterprise? (Each correct answer presents part of the solution. Choose two.)
Answer: A,B
Explanation:
For GitHub Copilot Business and Enterprise, prompts and suggestions are retained for 28 days to provide context and improve the service. User engagement data, which includes usage patterns and interactions, is kept for two years. This data retention policy is designed to balance service improvement with user privacy.
Reference: GitHub Copilot documentation on data privacy and retention policies for Business andEnterprise plans.
NEW QUESTION # 42
What is the best way to share feedback about GitHub Copilot Chat when using it on GitHub Mobile?
Answer: D
Explanation:
The best way to share feedback on GitHub Mobile is using the emojis in the Copilot Chat interface, which allows for direct and immediate feedback.
NEW QUESTION # 43
How can GitHub Copilot assist with code refactoring tasks?
Answer: D
Explanation:
GitHub Copilot can analyze existing code and suggest refactoring improvements to enhance code quality, readability, and maintainability. It can propose changes to improve code structure, reduce complexity, and follow best practices.
Reference: GitHub Copilot documentation on code refactoring assistance.
NEW QUESTION # 44
How does GitHub Copilot Chat help to fix security issues in your codebase?
Answer: B
Explanation:
"When Copilot Chat generates suggestions, it may annotate them with warnings about known insecure patterns, helping developers identify potential vulnerabilities in context." This indicates that Copilot Chat assists with security by annotating code suggestions with vulnerability patterns.
References: GitHub Copilot Chat security and responsible use documentation.
NEW QUESTION # 45
Identify the right use cases where GitHub Copilot Chat is most effective. (Each correct answer presents part of the solution. Choose two.)
Answer: B,C
Explanation:
GitHub Copilot Chat is effective for explaining and translating legacy code and generating unit test scenarios for new code.
NEW QUESTION # 46
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