1Z0-1127-25 OCI Generative AI Service Practice Question
A data scientist needs to fine-tune a large language model on a custom dataset of 10,000 prompt-completion pairs. They want to minimize cost while still updating the model effectively. Which fine-tuning technique is used by OCI Generative AI service?
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
OCI Generative AI uses T-Few, which updates only a small number of parameters via learned transformations, reducing computational cost while maintaining performance. Adapter, LoRA, and prefix tuning are general PEFT methods but not the specific technique offered by OCI.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Prefix tuning
Why it's wrong here
Prefix tuning is a PEFT technique, but not the one used by OCI Generative AI.
- ✓
T-Few fine-tuning
Why this is correct
T-Few is the parameter-efficient fine-tuning method provided by OCI GenAI service.
- ✗
Adapter fine-tuning
Why it's wrong here
Adapter is a different PEFT method; OCI uses T-Few.
- ✗
LoRA fine-tuning
Why it's wrong here
LoRA is another PEFT method, but OCI's offering is T-Few.
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