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GitHub GitHub-Copilot Exam Syllabus Topics:

SectionWeightObjectives
Testing with GitHub Copilot9%–15%- Generating tests and validation
- Verifying output quality and correctness
GitHub Copilot Plans and Features25%–31%- Configuration and settings
- Feature comparison and capabilities
- Free, Pro, Business, Enterprise plans
Responsible AI7%–20%- Legal and compliance considerations
- Ethical and safe use of AI tools
Privacy Fundamentals and Context Exclusions15%–20%- Protecting sensitive information
- Configuring context exclusion rules
How GitHub Copilot Works and Handles Data10%–15%- Context collection and privacy controls
- Architecture and underlying technology
- Data processing, retention, and usage policies
Developer Use Cases for AI14%–20%- Refactoring, documentation, and testing
- Code generation and completion
- Integration with IDEs and workflows
Prompt Crafting and Prompt Engineering9%–15%- Iterative improvement and context management
- Designing effective prompts

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GitHub CopilotCertification Exam Sample Questions (Q15-Q20):

NEW QUESTION # 15
Select a strategy to increase the performance of GitHub Copilot Chat.

Answer: B

Explanation:
Applying prompt engineering techniques to be more specific is the best strategy to increase the performance and relevance of GitHub Copilot Chat's responses.


NEW QUESTION # 16
Which Copilot Individual features are available when using a supported extension for Visual Studio, VS Code, or JetBrains IDEs? (Each correct answer presents part of the solution. Choose two.)

Answer: C,D


NEW QUESTION # 17
What content can be configured to be excluded with content exclusions? (Each correct answer presents part of the solution. Choose three.)

Answer: B,D,E

Explanation:
Content exclusions allow you to exclude files, folders, and repositories from being used by GitHub Copilot.


NEW QUESTION # 18
What method can a developer use to generate sample data with GitHub Copilot? (Each correct answer presents part of the solution. Choose two.)

Answer: A,C

Explanation:
GitHub Copilot can generate sample data by creating fictitious information based on patterns in its training data and by using suggestions based on API documentation within the repository.


NEW QUESTION # 19
You're introducing a new team member to GitHub Copilot. They ask you about the different ways to interact with or trigger Copilot's suggestions. Which of the following methods are valid ways to use or activate GitHub Copilot?

Answer: A,B,C,E

Explanation:
GitHub Copilot is designed to monitor developer input in real time. When a user writes a comment or starts typing code, Copilot predicts the intent and generates relevant suggestions inline. This method is particularly useful for quickly completing code blocks or writing repetitive logic.
Copilot Chat allows users to ask natural language questions directly in their IDE. This is useful for getting explanations, asking for code snippets, or even requesting refactoring suggestions - all within a conversational UI powered by generative AI.
GitHub Copilot supports the ability to trigger multiple suggestions at once. For example, by using shortcuts like Ctrl + Enter in Visual Studio Code, developers can cycle through multiple completions, improving the quality of the final code by choosing the most suitable option.
GitHub Copilot CLI allows users to leverage Copilot's assistance directly from the terminal. It can generate shell commands, explain terminal errors, or help write automation scripts, making it a powerful tool for command-line-focused developers.
Reference:
https://learn.microsoft.com/en-us/training/modules/introduction-to-github-Copilot/3-interacting-with-Copilot


NEW QUESTION # 20
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