1Z0-1127-25 OCI Generative AI Service Practice Question
A machine learning engineer is fine-tuning a Cohere Command R model using T-Few. They have prepared a JSONL dataset with 500 prompt-completion pairs. After submitting the fine-tuning job, they notice the model's performance on validation data is poor. Which action is MOST likely to improve 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
✓
Adding more high-quality training examples to the dataset
T-Few is parameter-efficient and may require more data. Increasing dataset size or using data augmentation is a likely improvement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Adding more high-quality training examples to the dataset
Why this is correct
More data can improve T-Few fine-tuning performance.
- ✗
Increasing the number of training epochs
Why it's wrong here
May lead to overfitting with limited data.
- ✗
Setting the temperature to 0 in the fine-tuning configuration
Why it's wrong here
Temperature is an inference parameter, not a training parameter.
- ✗
Switching to a larger base model like Llama 3 70B
Why it's wrong here
Larger model may help but is not directly addressing the data issue.
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