1Z0-1127-25 Using OCI Generative AI Service Practice Question
A data scientist wants to fine-tune a generative AI model on proprietary customer data. What is a best practice for preparing the training dataset?
⚠ Common exam trap
Oracle often tests the misconception that more data (random or public) is always better for fine-tuning, when in fact curated, domain-specific data with clear input-output pairs is essential for effective adaptation without degrading base model capabilities.
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
✓
Curate a dataset of domain-specific examples with clear input-output pairs.
Fine-tuning a generative AI model on proprietary data requires a curated, domain-specific dataset with clear input-output pairs. This ensures the model learns the desired task (e.g., summarization, classification) without introducing noise or irrelevant patterns, which is critical for OCI Generative AI Service fine-tuning where data quality directly impacts model performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Randomly sample 1000 records from production logs.
Why it's wrong here
Random sampling may not cover diverse scenarios needed for robust fine-tuning.
- ✗
Use the same dataset as the base model's pre-training data.
Why it's wrong here
That would not add new knowledge; fine-tuning should provide new, specific data.
- ✓
Curate a dataset of domain-specific examples with clear input-output pairs.
Why this is correct
Domain-specific curated data ensures the model learns the desired behavior for the target use case.
- ✗
Use the largest available public dataset from the internet.
Why it's wrong here
Public data may not be domain-relevant and can dilute proprietary knowledge.
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