1Z0-1127-25 LLM Fundamentals Practice Question
Which of the following is a distinguishing feature of in-context learning compared to fine-tuning?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
In-context learning does not update the model's weights; instead, examples are provided in the prompt
In-context learning does not update model weights; it provides examples in the prompt at inference time. Fine-tuning updates the model weights through additional training on a dataset.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
In-context learning modifies the model's weights based on examples
Why it's wrong here
In-context learning does not modify weights; it relies on the model's ability to learn from context in the prompt.
- ✓
In-context learning does not update the model's weights; instead, examples are provided in the prompt
Why this is correct
In-context learning uses examples in the prompt at inference time without any weight updates.
- ✗
In-context learning is only possible with encoder-only models
Why it's wrong here
In-context learning is commonly used with decoder-only models like GPT.
- ✗
In-context learning requires additional training on a labeled dataset
Why it's wrong here
That describes fine-tuning, not in-context learning.
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