1Z0-1127-25 Fundamentals of Large Language Models Practice Question
During inference with OCI Generative AI, you notice that the model is generating repetitive phrases. Which combination of parameters can help reduce repetition?
⚠ Common exam trap
Oracle often tests the misconception that lowering Top_p (making it more restrictive) reduces repetition, when in fact it can worsen repetition by limiting the model to only the most probable tokens, which are often the same ones already used.
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
✓
Top_p = 0.9, frequency_penalty = 0.5
A high Top_p value (0.9) allows the model to consider a diverse set of tokens, reducing the chance of getting stuck in repetitive loops, while a positive frequency_penalty (0.5) actively penalizes tokens that have already been generated, discouraging the model from repeating the same phrases. Together, these parameters balance creativity and repetition suppression.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Top_p = 0.1, frequency_penalty = 0.5
Why it's wrong here
Top_p = 0.1 severely limits the pool of possible tokens, often making output repetitive despite the penalty.
- ✓
Top_p = 0.9, frequency_penalty = 0.5
Why this is correct
This combination applies a gentle penalty on repeated tokens while keeping token selection diverse, effectively reducing repetition.
- ✗
Top_p = 0.9, frequency_penalty = 0.0
Why it's wrong here
Without any frequency penalty, the model has no incentive to avoid repetition.
- ✗
Top_p = 1.0, frequency_penalty = 0.0
Why it's wrong here
Top_p = 1.0 includes all tokens (no nucleus sampling) and no penalty, so repetition is likely.
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