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| Section | Weight | Objectives |
|---|---|---|
| How GitHub Copilot Works and Handles Data | 15% | - Privacy and data handling within Copilot - The prompt creation process - How Copilot gathers code context - AI model response generation - Understanding that private repository content is not used to train models |
| Prompt Engineering and AI Interaction | 9% | - Refining prompts for better coding results - Improving Copilot responses using context - Creating effective prompts - Crafting and refining prompts (prompt engineering) |
| 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 |
| Privacy Fundamentals and Context Exclusions | 15% | - Troubleshooting: missing suggestions, exclusions not applied, triggering Copilot - Safeguards: duplication detector, contractual protection, data collection settings - Content exclusions at repository and organization level - Effects and limitations of content exclusions - Ownership of AI-generated outputs |
| Developer Use Cases for AI | 14% | - Documentation generation - Code generation and refactoring - Automating repetitive development tasks - SDLC support and productivity insights - Learning new languages and frameworks - Debugging applications |
| Responsible AI with GitHub Copilot | 7% | - Ethical AI principles and responsible use - Risks and limitations of generative AI tools - Privacy and transparency considerations - Bias and fairness in AI-generated code - Validating AI output and mitigation strategies |
| 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 |
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NEW QUESTION # 34
What is used by GitHub Copilot in the IDE to determine the prompt context?
Answer: A
Explanation:
"GitHub Copilot generates suggestions based on context from the IDE, including the file you are editing, open tabs, cursor position, and any selected code." This proves that the context is drawn from editor state information, making option A correct.
References: GitHub Copilot context and suggestions documentation.
NEW QUESTION # 35
Which principle emphasizes that AI systems should be understandable and provide clear information on how they work?
Answer: A
Explanation:
The principle of transparency emphasizes that AI systems should be understandable and provide clear information about their operations. This ensures that users can understand how the AI arrives at its decisions and suggestions.
NEW QUESTION # 36
You recently ended an interactive GitHub Copilot CLI session.
You need to start a new session that meets the following requirements:
* Provides a list of previously saved interactive sessions
* Resumes the most recently ended session
Which command should you run for each requirement? Each command may be used once, more than once, or not at all.
Answer:
Explanation:
Explanation:
NEW QUESTION # 37
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 # 38
What role does the pre-processing of user input play in the data flow of GitHub Copilot Chat?
Answer: D
Explanation:
"Before being sent to the model, user input is pre-processed to enrich the prompt with contextual information such as open files, code selections, or previous conversation history." This shows that pre-processing is used to enrich input with context, making option B correct.
References: GitHub Copilot Chat architecture documentation.
NEW QUESTION # 39
......
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