Q: How does GitHub Copilot learn from your prompts?

A It only relies on its pre-existing training data.
B It utilizes various approaches including zero-shot, one-shot, and few-shot learning.
C It requires extensive manual training on specific tasks.
D It cannot learn and adapt based on user interactions.

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Answer: B

Explanation:

Copilot uses zero-shot, one-shot, and few-shot learning to adapt to different scenarios and generate relevant code.

GitHub Copilot learns from your prompts using zero-shot, one-shot, and few-shot learning techniques — approaches that help the AI understand and generate relevant code without requiring large amounts of new training data.

In zero-shot learning, Copilot relies purely on its existing knowledge to respond to your prompt, even if it has never seen a similar example before.
In one-shot learning, the model uses a single example to understand what you want and then generalizes the idea to new tasks.
In few-shot learning, it learns from a few examples within your prompt to detect the pattern and produce accurate, context-aware code.

These methods make Copilot flexible and powerful, allowing it to quickly adapt to different coding situations, understand your intent, and offer meaningful code completions — all in real time.

Practice Assessment for GH-300: GitHub Copilot

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Q: How does GitHub Copilot learn from your prompts?

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