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Fundamentals of Large Language ModelshardMultiple ChoiceObjective-mapped

1Z0-1127-25 Fundamentals of Large Language Models Practice Question

A research team is experimenting with few-shot prompting to improve a model's performance on a complex reasoning task. They find that the model's performance degrades when the few-shot examples are too similar to each other. What is the likely cause and best remedy?

⚠ Common exam trap

Oracle often tests the misconception that more examples always improve performance, when in fact diversity is critical to prevent overfitting in few-shot prompting.

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

The examples lack diversity, causing the model to overfit to a narrow pattern. Use more diverse examples.

When few-shot examples are too similar, the model overfits to a narrow pattern, reducing its ability to generalize to the diverse reasoning paths required by the task. This is a known limitation of in-context learning: the model treats the examples as a template rather than as diverse demonstrations. Using more diverse examples exposes the model to a wider range of reasoning patterns, improving robustness.

Answer analysis

Option-by-option breakdown

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

  • The model has not seen enough examples. Increase the number of few-shot examples.

    Why it's wrong here

    More similar examples may not help; diversity is key.

  • The examples are presented in a confusing order. Reorder them by difficulty.

    Why it's wrong here

    Order is less impactful than diversity.

  • The examples lack diversity, causing the model to overfit to a narrow pattern. Use more diverse examples.

    Why this is correct

    Diverse examples reduce bias and improve generalization.

  • The temperature is too low, making the model too deterministic. Increase temperature slightly.

    Why it's wrong here

    Temperature does not address example similarity.

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