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| Section | Weight | Objectives |
|---|---|---|
| Testing with GitHub Copilot | 9% | - Generating unit tests and integration tests - Improving test coverage with AI suggestions - Identifying edge cases - Editor configuration and org-level suggestion settings |
| 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% | - Refining prompts for better coding results - Creating effective prompts - Crafting and refining prompts (prompt engineering) - Improving Copilot responses using context |
| 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 |
| Privacy Fundamentals and Context Exclusions | 15% | - Effects and limitations of content exclusions - Ownership of AI-generated outputs - Safeguards: duplication detector, contractual protection, data collection settings - Content exclusions at repository and organization level - Troubleshooting: missing suggestions, exclusions not applied, triggering Copilot |
| Developer Use Cases for AI | 14% | - Debugging applications - Automating repetitive development tasks - Code generation and refactoring - Documentation generation - Learning new languages and frameworks - SDLC support and productivity insights |
| GitHub Copilot Plans and Features | 31% | - Implementing and managing Copilot plans for an organization - Copilot Chat and code suggestion capabilities - Configuration options and productivity features - IDE integration (VS Code, Visual Studio, JetBrains) - Differences between Copilot Individual, Business, and Enterprise plans |
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NEW QUESTION # 88
1.
blog.yatricloud.com
blog.yatricloud.com
Answer: A,C
Explanation:
The GitHub Copilot usage metrics API provides insights into the acceptance rate of code suggestions and Copilot Chat specific suggestions acceptance metrics, helping organizations evaluate its effectiveness.
Reference: GitHub Copilot usage metrics API documentation.
NEW QUESTION # 89
What are the potential limitations of GitHub Copilot in maintaining existing codebases?
Answer: A
Explanation:
"Copilot may not always understand the full context or complex dependencies in large codebases, and suggestions may require significant review." This proves that the main limitation is a lack of deep awareness of complex or large codebase contexts.
References: GitHub Copilot limitations documentation.
NEW QUESTION # 90
How does GitHub Copilot suggest code optimizations for improved performance?
Answer: B
Explanation:
GitHub Copilot suggests code optimizations by analyzing the codebase and recommending more efficient algorithms or data structures.
Reference: GitHub Copilot code optimization documentation.
NEW QUESTION # 91
You have a GitHub Copilot subscription.
You need to start an interactive GitHub Copilot CLI session from the root folder of a project.
If you are NOT authenticated, GitHub Copilot must prompt you to sign in.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
NEW QUESTION # 92
What are the effects of content exclusions? (Each correct answer presents part of the solution. Choose two.)
Answer: A,B
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 # 93
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