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LLM FundamentalshardMultiple ChoiceObjective-mapped

1Z0-1127-25 LLM Fundamentals Practice Question

An OCI user observes that their Mistral model produces very repetitive text when temperature is set to 0.9 and top-p to 1.0. Which adjustment is most likely to reduce repetition?

⚠ Common exam trap

A common mistake in Oracle OCI GenAI is assuming that reducing randomness (lower top-p or top-k) will fix repetition, when it often makes it worse, and that increasing temperature helps, but it risks incoherence. Frequency penalty is the targeted solution.

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

Enable frequency penalty

Enabling frequency penalty directly reduces the likelihood of the model repeating the same tokens by subtracting a penalty proportional to the token's existing frequency in the generated text. This is the most targeted way to combat repetitive output without altering the core sampling parameters (temperature and top-p) that control randomness.

Answer analysis

Option-by-option breakdown

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

  • Decrease top-p to 0.5

    Why it's wrong here

    Lower top-p restricts vocabulary but may not reduce repetition.

  • Increase temperature to 1.5

    Why it's wrong here

    Higher temperature increases randomness but may not reduce repetition.

  • Enable frequency penalty

    Why this is correct

    Frequency penalty penalizes tokens based on their frequency in the generated text.

  • Set top-k to 1

    Why it's wrong here

    Top-k=1 is greedy, which increases repetition.

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