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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Understand GitHub Copilot Data and Architecture | 10-15% | - Pipeline, MCP, context gathering, data handling |
| Topic 2: Improve Developer Productivity | 10-15% | - Agent workflows, productivity, SDLC integration |
| Topic 3: Responsible AI | 15-20% | - Ethics, validation, bias mitigation, transparency |
| Topic 4: GitHub Copilot Features | 25-30% | - Multi-model, Agent Mode, Mission Control, BYOK |
| Topic 5: Configure Privacy, Exclusions, Safeguards | 10-15% | - Agent permissions, content exclusions, org policies |
| Topic 6: Apply Prompt Engineering and Context | 10-15% | - Model-specific prompting, context files, best practices |
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NEW QUESTION # 15
What content can be configured to be excluded with content exclusions? (Each correct answer presents part of the solution. Choose three.)
Answer: A,C,D
Explanation:
Content exclusions allow you to exclude files, folders, and repositories from being used by GitHub Copilot.
NEW QUESTION # 16
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: B,C,D,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 # 17
What is a benefit of using custom models in GitHub Copilot?
Answer: C
Explanation:
Custom models in GitHub Copilot allow the tool to learn from the specific code patterns and practices within your repositories. This results in suggestions that are more aligned with your organization's coding standards and conventions, improving the relevance and accuracy of the generated code.
NEW QUESTION # 18
What is used by GitHub Copilot in the IDE to determine the prompt context?
Answer: A
Explanation:
Copilot gathers context directly from your IDE - including which files or tabs are open, where your cursor is positioned, and any selected code - to tailor its suggestions to exactly where and how you're coding.
NEW QUESTION # 19
How can the concept of fairness be integrated into the process of operating an AI tool?
Answer: A
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 # 20
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