1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A company has deployed a generative AI model on OCI to generate product descriptions. After a recent update, the model started producing outputs with repetitive phrases and poor coherence. The inference endpoint is configured with default parameters. Which single parameter adjustment is most likely to improve output quality?
⚠ Common exam trap
Many candidates confuse frequency penalty with temperature or top-p, assuming that increasing randomness (temperature) or narrowing token selection (top-p) will fix repetition, when in fact those parameters address different aspects of output diversity and coherence.
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
✓
Increase the frequency penalty parameter to 0.5
Increasing the frequency penalty reduces the likelihood of the model repeating the same phrases, directly addressing the repetitive outputs. The frequency penalty subtracts a proportional penalty from tokens that have already appeared, discouraging repetition and improving coherence. Default parameters often have no frequency penalty (0.0), so a small positive value like 0.5 can significantly enhance output diversity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the max-tokens parameter to 512
Why it's wrong here
Increasing max tokens only allows longer outputs, not fixing repetition.
- ✓
Increase the frequency penalty parameter to 0.5
Why this is correct
Frequency penalty reduces repeated tokens, directly improving repetitive output.
- ✗
Increase the temperature parameter to 1.5
Why it's wrong here
Increasing temperature increases randomness, which may worsen coherence and does not directly address repetition.
- ✗
Decrease the top-p parameter to 0.8
Why it's wrong here
Decreasing top-p narrows token selection but does not penalize repetition.
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