1Z0-1127-25 OCI Generative AI Service Practice Question
Which statement accurately describes the T-Few fine-tuning technique used in OCI Generative AI?
⚠ Common exam trap
Many candidates confuse parameter-efficient fine-tuning (PEFT) with full fine-tuning or prompting, leading them to select options that describe full parameter updates or no training at all, rather than recognizing T-Few as a lightweight adaptation method.
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 is a parameter-efficient fine-tuning method that updates only a fraction of the model parameters.
The T-Few fine-tuning technique is a parameter-efficient fine-tuning (PEFT) method that updates only a small fraction of the model's parameters, typically by introducing and training adapter layers or using low-rank updates. This approach significantly reduces computational and memory requirements compared to full fine-tuning, making it suitable for adapting large language models with limited resources. In OCI Generative AI, T-Few enables efficient customization without retraining the entire model.
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 automatically adjusts hyperparameters during inference.
Why it's wrong here
Hyperparameters are set before training; T-Few does not adjust them during inference.
- ✗
It does not require any training data and works by prompting only.
Why it's wrong here
T-Few requires a training dataset with prompt/completion pairs.
- ✗
It updates all model parameters, requiring substantial compute resources.
Why it's wrong here
T-Few updates only a small subset of parameters (e.g., via adapter layers).
- ✓
It is a parameter-efficient fine-tuning method that updates only a fraction of the model parameters.
Why this is correct
T-Few uses low-rank adaptations to efficiently fine-tune models.
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