1Z0-1127-25 OCI Generative AI Service Practice Question
A data scientist needs to fine-tune a model using OCI Generative AI. They have prepared a dataset in JSONL format with prompt/completion pairs. The fine-tuning job is configured with the T-Few technique. What is a key characteristic of T-Few fine-tuning?
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
✓
It modifies a small subset of parameters using lightweight adapter layers
T-Few is a parameter-efficient fine-tuning method that updates only a small fraction of model parameters, making it faster and more resource-efficient than full fine-tuning. It does not train all parameters, add new layers, or require unlabeled data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It requires unlabeled data for unsupervised pre-training before fine-tuning
Why it's wrong here
T-Few is a supervised fine-tuning technique that uses labeled prompt/completion pairs, not unlabeled data.
- ✗
It updates all model parameters, requiring significant compute resources
Why it's wrong here
T-Few is parameter-efficient and only updates a small subset of parameters.
- ✓
It modifies a small subset of parameters using lightweight adapter layers
Why this is correct
T-Few uses adapter-based fine-tuning that updates a small number of parameters while keeping most of the model frozen.
- ✗
It only trains a new classification head on top of a frozen base model
Why it's wrong here
T-Few updates internal parameters via adapter-like transformations, not just a classification head.
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