1Z0-1127-25 Prompt Engineering Practice Question
A data scientist is using OCI Generative AI to generate synthetic data for training. They observe that the model's outputs lack diversity and often repeat the same phrases. Which combination of parameter adjustments would BEST increase output diversity?
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 temperature to 0.9 and increase top-p to 0.9
Increasing temperature and top-p both increase randomness and diversity. Temperature controls the randomness of token selection, while top-p (nucleus sampling) allows a broader set of probable tokens.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set frequency penalty to 0.0 and presence penalty to 0.0
Why it's wrong here
Zero penalties do nothing to increase diversity; they may even allow repetition.
- ✓
Increase temperature to 0.9 and increase top-p to 0.9
Why this is correct
Both higher temperature and higher top-p increase randomness and token variety, boosting diversity.
- ✗
Decrease temperature to 0.3 and increase top-p to 0.9
Why it's wrong here
Lower temperature makes outputs more deterministic, reducing diversity.
- ✗
Increase temperature to 0.9 and decrease top-p to 0.5
Why it's wrong here
Decreasing top-p reduces diversity by narrowing the set of considered tokens.
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