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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering and AI Interaction | 9% | - Improving Copilot responses using context - Creating effective prompts - Crafting and refining prompts (prompt engineering) - Refining prompts for better coding results |
| How GitHub Copilot Works and Handles Data | 15% | - The prompt creation process - Understanding that private repository content is not used to train models - How Copilot gathers code context - AI model response generation - Privacy and data handling within Copilot |
| GitHub Copilot Plans and Features | 31% | - Copilot Chat and code suggestion capabilities - Differences between Copilot Individual, Business, and Enterprise plans - Configuration options and productivity features - IDE integration (VS Code, Visual Studio, JetBrains) - Implementing and managing Copilot plans for an organization |
| Responsible AI with GitHub Copilot | 7% | - Ethical AI principles and responsible use - Validating AI output and mitigation strategies - Risks and limitations of generative AI tools - Bias and fairness in AI-generated code - Privacy and transparency considerations |
| Privacy Fundamentals and Context Exclusions | 15% | - Ownership of AI-generated outputs - Content exclusions at repository and organization level - Troubleshooting: missing suggestions, exclusions not applied, triggering Copilot - Effects and limitations of content exclusions - Safeguards: duplication detector, contractual protection, data collection settings |
| Testing with GitHub Copilot | 9% | - Identifying edge cases - Improving test coverage with AI suggestions - Editor configuration and org-level suggestion settings - Generating unit tests and integration tests |
| Developer Use Cases for AI | 14% | - Learning new languages and frameworks - Documentation generation - SDLC support and productivity insights - Debugging applications - Automating repetitive development tasks - Code generation and refactoring |
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NEW QUESTION # 49
How can GitHub Copilot be limited when it comes to suggesting unit tests?
Answer: C
Explanation:
GitHub Copilot often suggests basic unit tests and may not cover all edge cases or complex integration scenarios, requiring developers to supplement its suggestions.
Reference: GitHub Copilot testing limitations.
NEW QUESTION # 50
What are the potential risks associated with relying heavily on code generated from GitHub Copilot? (Each correct answer presents part of the solution. Choose two.)
Answer: A,D
Explanation:
Heavy reliance on GitHub Copilot can introduce security vulnerabilities if the generated code contains known exploits. Additionally, Copilot's suggestions may not always align with best practices or the latest standards, requiring careful review and validation.
Reference: GitHub Copilot best practices and risk management.
NEW QUESTION # 51
Which GitHub Copilot pricing plans include features that exclude your GitHub Copilot data like usage, prompts, and suggestions from default training GitHub Copilot? (Choose two correct answers.)
Answer: B,D
Explanation:
"For Copilot Business and Copilot Enterprise, user data such as code suggestions, prompts, and completions are excluded from training GitHub Copilot's models." This confirms that only Business and Enterprise plans provide this exclusion feature.
References: GitHub Copilot data usage and privacy documentation.
NEW QUESTION # 52
What are the effects of content exclusions? (Each correct answer presents part of the solution. Choose two.)
Answer: B,C
Explanation:
Content exclusions prevent GitHub Copilot from using the excluded content as context and stop suggestions from being generated in those files.
Reference: GitHub Copilot content exclusion documentation.
NEW QUESTION # 53
How can the concept of fairness be integrated into the process of operating an AI tool?
Answer: D
Explanation:
Fairness in AI tools is achieved by training the data and algorithms to be free from biases. This ensures that the tool treats all users equitably and avoids discriminatory outcomes.
NEW QUESTION # 54
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