1Z0-1127-25 Using OCI Generative AI Service Practice Question
An application using OCI Generative AI produces inconsistent responses to the same user query. The developer suspects the model's output variability is too high. Which parameter adjustment would most directly reduce output randomness?
⚠ Common exam trap
Watch out — candidates often confuse top_p and temperature, assuming both control randomness similarly, but top_p controls the diversity of the candidate pool while temperature directly sharpens or flattens the probability distribution.
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
✓
Decrease the temperature parameter.
Temperature directly controls the randomness of token sampling in the model's output distribution. Lowering temperature (e.g., from 0.7 to 0.2) makes the model more deterministic by concentrating probability mass on the most likely next tokens, thus reducing output variability for the same query.
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.
Why it's wrong here
Max tokens affects length, not randomness.
- ✗
Increase the top_p parameter.
Why it's wrong here
Increasing top_p increases diversity, not reduces it.
- ✗
Change the model to a smaller variant.
Why it's wrong here
Model size doesn't directly control randomness; temperature does.
- ✓
Decrease the temperature parameter.
Why this is correct
Lower temperature reduces randomness, making responses more consistent.
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