1Z0-1127-25 OCI Generative AI Service Practice Question
A data scientist needs to fine-tune a Llama 3 model for a legal document classification task. They have a dataset of 10,000 labeled examples. Which fine-tuning technique available in OCI Generative AI is most suitable for efficiently adapting the model with limited computational overhead?
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
✓
T-Few fine-tuning
T-Few fine-tuning is a parameter-efficient technique that updates only a small number of weights, making it suitable for fine-tuning large models with limited compute. It is the technique offered by OCI GenAI for fine-tuning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Full fine-tuning all model parameters
Why it's wrong here
Full fine-tuning is computationally expensive and requires significant resources, not efficient for a 10k dataset.
- ✗
LoRA (Low-Rank Adaptation)
Why it's wrong here
LoRA is not natively available as a fine-tuning method in OCI GenAI; T-Few is the documented technique.
- ✗
Prefix tuning
Why it's wrong here
Prefix tuning is not the standard fine-tuning method offered by OCI GenAI.
- ✓
T-Few fine-tuning
Why this is correct
T-Few is an efficient parameter-update technique designed for fine-tuning with limited compute, available in OCI GenAI.
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