Free NVIDIA NCA-GENL Actual Exam Questions - Question 1 Discussion

Maybe D is out since few-shot learning isn’t about full fine-tuning on huge datasets. C doesn’t really fit either because hyperparameter optimization is a different process. Between A and B, B involves training from scratch which few-shot definitely doesn’t do. So yeah, A makes the most sense because it’s about showing examples directly in the prompt to guide the model, not retraining it.

B tbh, few-shot learning isn't about training from scratch or fine-tuning on large data. It’s more about giving the model a few examples within the prompt itself, so A fits best by process of elimination.

D imo, the other options talk about training or fine-tuning, which is way beyond what few-shot learning means here. It’s really about showing the model a few examples right in the prompt to help it understand the task better without changing its underlying weights. So A fits perfectly since it’s about giving examples directly, not retraining or optimizing hyperparameters.

It’s A because few-shot learning is about giving the model a few examples right in the prompt to guide responses, not retraining or fine-tuning like in B or D.

A makes the most sense here since few-shot learning means giving the model a couple examples to learn from in the prompt itself. Others don’t fit the quick adaptation idea.