GH-300: Prompts, Knowledge & Context
Prompts, Knowledge & Context is one of the skill areas tested on the GitHub Copilot (GH-300) exam. Below are free GH-300 practice questions with worked answers and a concept diagram — each with a plain-language explanation. Practice the first 10 questions of GH-300 free, no signup.
The concept, in one picture
2 free GH-300 Prompts, Knowledge & Context questions
Why might an organization choose to use custom models with GitHub Copilot?
- Suggestions are generated and displayed noticeably faster.
- ✓ Suggestions reflect the coding practices and patterns found in the organization's own repositories.
- Suggestions run on the organization's private LLM hardware.
- Suggestions are guaranteed to be correct.
A custom model is trained on the organization's own repositories, so its suggestions pick up the team's established patterns and conventions and feel more relevant than a generic model's output.
- Custom models do not promise faster responses, dedicated org hardware, or guaranteed-correct code.
For GitHub Copilot Individual, which two statements correctly describe how prompt data is used? (Each correct answer presents part of the solution. Choose two.)
- Prompt data is kept unencrypted so it can be processed more quickly.
- GitHub uses prompt data internally to improve its search engine.
- ✓ Prompt data may be used to train machine learning models that improve code suggestions.
- ✓ Live user input is used to produce context-aware code suggestions.
With Copilot Individual, prompts and completions may be retained to train the machine learning models that make Copilot's suggestions better, and the real-time input a developer provides drives context-aware completions in the moment.
- Storing data unencrypted for speed and feeding a search engine are not how Copilot Individual handles prompt data.
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