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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: GitHub Copilot Plans and Features | 31% | - IDE integration (VS Code, Visual Studio, JetBrains) - Configuration options and productivity features - Implementing and managing Copilot plans for an organization - Copilot Chat and code suggestion capabilities - Differences between Copilot Individual, Business, and Enterprise plans |
| Topic 2: Privacy Fundamentals and Context Exclusions | 15% | - Ownership of AI-generated outputs - 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 |
| Topic 3: How GitHub Copilot Works and Handles Data | 15% | - AI model response generation - Privacy and data handling within Copilot - The prompt creation process - Understanding that private repository content is not used to train models - How Copilot gathers code context |
| Topic 4: Testing with GitHub Copilot | 9% | - Improving test coverage with AI suggestions - Editor configuration and org-level suggestion settings - Identifying edge cases - Generating unit tests and integration tests |
| Topic 5: Developer Use Cases for AI | 14% | - Debugging applications - Learning new languages and frameworks - Code generation and refactoring - Automating repetitive development tasks - Documentation generation - SDLC support and productivity insights |
| Topic 6: Prompt Engineering and AI Interaction | 9% | - Improving Copilot responses using context - Creating effective prompts - Refining prompts for better coding results - Crafting and refining prompts (prompt engineering) |
| Topic 7: Responsible AI with GitHub Copilot | 7% | - Risks and limitations of generative AI tools - Ethical AI principles and responsible use - Privacy and transparency considerations - Bias and fairness in AI-generated code - Validating AI output and mitigation strategies |
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NEW QUESTION # 99
In what ways can GitHub Copilot contribute to the design phase of the Software Development Life Cycle (SDLC)?
Answer: A
Explanation:
"Copilot can assist in the design phase by suggesting design patterns, frameworks, and best practices relevant to the context of your project." This shows Copilot contributes by offering design-related recommendations, not by independently producing full designs or managing collaboration.
References: GitHub Copilot use case documentation.
NEW QUESTION # 100
How can GitHub Copilot aid developers in writing documentation for their code?
Answer: C
Explanation:
"Copilot can suggest comments and documentation summaries that describe the functionality of the code being written." This makes option C correct, as Copilot provides summaries or descriptions rather than full automatic documentation.
References: GitHub Copilot documentation features.
NEW QUESTION # 101
What configuration needs to be set to get help from Microsoft and GitHub protecting against IP infringement while using GitHub Copilot?
Answer: A
Explanation:
To help protect against IP infringement, you need to configure GitHub Copilot to block suggestions that match public code. This ensures that the generated code is not directly copied from publicly available sources.
Reference: GitHub Copilot documentation on IP protection and code filtering.
NEW QUESTION # 102
Which of the following statements best describes the impact of GitHub Copilot on the software development process?
Answer: B
Explanation:
GitHub Copilot primarily impacts the software development process by increasing productivity through automating repetitive coding tasks.
Reference: GitHub Copilot impact documentation.
NEW QUESTION # 103
What is the impact of the "Fill-In-the-Middle" (FIM) technique on GitHub Copilot's code suggestions?
Answer: B
Explanation:
"Fill-in-the-Middle (FIM) enables Copilot to consider both prefix and suffix code, generating more accurate suggestions for the missing middle portion." This makes option D correct, as it explains how FIM enhances suggestion accuracy.
References: GitHub Copilot model training and FIM technique documentation.
NEW QUESTION # 104
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