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LLM FundamentalshardMultiple ChoiceObjective-mapped

1Z0-1127-25 LLM Fundamentals Practice Question

A team is building a code generation assistant and needs to choose between fine-tuning a base LLM or using in-context learning with a few examples. They have 500 high-quality code examples. The assistant must generate code for a wide variety of tasks. Which approach is BETTER and why?

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, because it allows the model to adapt to each task dynamically without risking catastrophic forgetting

Fine-tuning with 500 examples may lead to overfitting or catastrophic forgetting, especially when the tasks are diverse. In-context learning with a few examples per task is more flexible and leverages the model's pre-trained knowledge. The small dataset size makes fine-tuning risky.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Fine-tuning, because it reduces inference cost compared to providing examples each time

    Why it's wrong here

    While fine-tuning may reduce prompt length, the cost savings are marginal compared to the risk of poor performance on diverse tasks.

  • Fine-tuning, because it permanently encodes the examples into the model weights

    Why it's wrong here

    With only 500 examples, fine-tuning is likely to overfit and forget other capabilities, making it less robust for diverse tasks.

  • In-context learning, because it allows the model to adapt to each task dynamically without risking catastrophic forgetting

    Why this is correct

    In-context learning uses the model's existing knowledge and adapts via examples in the prompt, which is more flexible for diverse tasks with a small dataset.

  • In-context learning, because it requires no additional training infrastructure

    Why it's wrong here

    Although this is a benefit, the primary reason is preserving model versatility; the team likely has training infrastructure available.

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