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| Section | Weight | Objectives |
|---|---|---|
| How GitHub Copilot Works and Handles Data | 15% | - Privacy and data handling within Copilot - AI model response generation - The prompt creation process - Understanding that private repository content is not used to train models - How Copilot gathers code context |
| Prompt Engineering and AI Interaction | 9% | - Improving Copilot responses using context - Crafting and refining prompts (prompt engineering) - Refining prompts for better coding results - Creating effective prompts |
| Responsible AI with GitHub Copilot | 7% | - Bias and fairness in AI-generated code - Validating AI output and mitigation strategies - Ethical AI principles and responsible use - Risks and limitations of generative AI tools - Privacy and transparency considerations |
| GitHub Copilot Plans and Features | 31% | - IDE integration (VS Code, Visual Studio, JetBrains) - 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 |
| Developer Use Cases for AI | 14% | - Debugging applications - Automating repetitive development tasks - Learning new languages and frameworks - Code generation and refactoring - SDLC support and productivity insights - Documentation generation |
| Privacy Fundamentals and Context Exclusions | 15% | - Effects and limitations of content exclusions - Safeguards: duplication detector, contractual protection, data collection settings - Content exclusions at repository and organization level - Ownership of AI-generated outputs - Troubleshooting: missing suggestions, exclusions not applied, triggering Copilot |
| Testing with GitHub Copilot | 9% | - Editor configuration and org-level suggestion settings - Generating unit tests and integration tests - Improving test coverage with AI suggestions - Identifying edge cases |
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NEW QUESTION # 47
Which Copilot Enterprise features are available in all commercially supported IDEs?
Answer: C,D
Explanation:
"Copilot Enterprise provides inline code suggestions and Copilot Chat in all commercially supported IDEs.
Additional features, such as pull request summaries and knowledge bases, are available in GitHub.com." This confirms that inline suggestions and Chat are the features consistently available in IDEs.
References: GitHub Copilot Enterprise feature documentation.
NEW QUESTION # 48
Why is code reviewing still necessary when using GitHub Copilot to write tests?
Answer: D
Explanation:
Code review is necessary because GitHub Copilot's generated test cases might not cover all possible scenarios, especially edge cases and complex interactions.
NEW QUESTION # 49
Which of the following prompts can be used to guide GitHub Copilot Chat in refactoring code for quality improvements? (Each correct answer presents part of the solution. Choose two.)
Answer: B,D
Explanation:
Effective prompts for refactoring include requests that focus on specific quality improvements, such as readability and maintainability. These prompts guide GitHub Copilot to provide relevant and actionable suggestions.
Reference: GitHub Copilot prompt engineering best practices.
NEW QUESTION # 50
How can GitHub Copilot assist in maintaining consistency across your tests?
Answer: B
Explanation:
"Copilot learns from the patterns in your existing tests and suggests similar structures, which helps maintain consistency across the test suite." This confirms that Copilot supports test consistency through pattern recognition and suggestion.
References: GitHub Copilot testing documentation.
NEW QUESTION # 51
Which of the following GitHub Copilot Business related activities can be tracked using the organization audit logs?
Answer: A
Explanation:
Organization audit logs track changes to content exclusion settings, providing administrators with visibility into configuration changes.
Reference: GitHub Copilot Business audit logs.
NEW QUESTION # 52
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