1Z0-1127-25 Using OCI Generative AI Service Practice Question
An e-commerce company uses OCI Generative AI to generate product descriptions. They have fine-tuned the model on their product catalog. They notice that the descriptions are accurate but lack creativity and are repetitive. They want to maintain accuracy while adding variety. What change should they make?
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 temperature from 0.2 to 0.5.
Slightly increasing temperature (e.g., from 0.2 to 0.5) introduces controlled variability without significantly compromising accuracy. Option A is wrong because top_p=1.0 samples from the full distribution, which can add noise. Option C is wrong because adding more training data requires effort and time, and may not immediately add variety. Option D is wrong because changing the base model could hurt accuracy and requires retraining.
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 top_p sampling from 0.9 to 1.0.
Why it's wrong here
Top_p=1.0 includes all tokens, which can increase randomness but may reduce accuracy.
- ✓
Increase the temperature from 0.2 to 0.5.
Why this is correct
A moderate temperature increase adds variety while preserving factual accuracy.
- ✗
Use a different base model.
Why it's wrong here
Switching models risks losing accuracy gained from fine-tuning.
- ✗
Add more training examples with diverse descriptions.
Why it's wrong here
Adding data requires curation and retraining, and is not a quick tuning parameter.
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Senior Network & Security Engineer · founder of Courseiva
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