1Z0-1127-25 Deploying and Managing Generative AI on OCI Practice Question
A company is using OCI Generative AI service to generate product descriptions. They notice that the model sometimes generates biased content. Which approach should they take to mitigate bias while maintaining performance?
⚠ Common exam trap
Oracle often tests the misconception that post-processing or model swapping is a sufficient fix for bias, when in fact only fine-tuning or retraining can address the root cause without sacrificing performance.
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
✓
Fine-tune the model with a balanced, curated dataset that reduces bias
Fine-tuning with a balanced, curated dataset directly addresses the root cause of bias by adjusting the model's internal weights to reduce reliance on biased patterns in the original training data. This approach preserves the model's generative performance for product descriptions because it retrains only on domain-specific, unbiased examples, unlike post-processing which merely filters outputs without correcting the underlying model behavior.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Fine-tune the model with a balanced, curated dataset that reduces bias
Why this is correct
Fine-tuning allows adjusting model behavior.
- ✗
Use a larger model without fine-tuning
Why it's wrong here
Larger models can still exhibit bias.
- ✗
Post-process outputs to remove biased phrases
Why it's wrong here
This is a reactive approach and may miss nuanced bias.
- ✗
Switch to a different pre-built model from OCI
Why it's wrong here
Other pre-built models may also have bias.
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