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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Developer Productivity with Copilot | - Workflow optimization
| |
| Topic 2: GitHub Copilot Data, Privacy, and Architecture | - Data handling and privacy safeguards
| |
| Topic 3: Prompt Engineering and Context Crafting | - Effective prompting techniques
| |
| Topic 4: Responsible AI | 7% | - Explain responsible usage of AI
|
| Topic 5: GitHub Copilot Plans and Features | 31% | - Copilot plans comparison
|
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NEW QUESTION # 128
When using GitHub Copilot to identify missing tests in your codebase, which of the following is the most important factor to consider?
Answer: D
Explanation:
GitHub Copilot needs the relevant code and test files in its context window to spot where tests are missing - without that context, it can't accurately identify gaps.
NEW QUESTION # 129
When using an IDE with a supported GitHub Copilot plug-in, which Chat features can be accessed from within the IDE? Each correct answer presents part of the solution. (Choose two.)
Answer: B,D
Explanation:
Within your IDE's Copilot Chat pane you can ask it to explain existing code and suggest improvements.
You can also prompt it to generate unit tests for your code directly in the editor.
NEW QUESTION # 130
Which of the following is a risk associated with using AI?
Answer: B
Explanation:
A risk associated with AI is that its decisions can be difficult to interpret, leading to a lack of transparency and potential misunderstandings.
NEW QUESTION # 131
If you are working on open source projects, GitHub Copilot Individual can be paid:
Answer: D
NEW QUESTION # 132
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 # 133
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