Free NVIDIA NCA-GENL Actual Exam Questions - Question 8 Discussion

Makes sense to rule out A, C, and D since they don’t really measure how well the model performs after fine-tuning. B fits best as it directly checks improvement on actual data. B

D imo, since model size and layers are fixed and don’t show performance. Training time isn’t a performance metric either, so validating accuracy is the best way to see if fine-tuning worked.

B definitely, since accuracy shows real improvement, not just effort or size.

Probably B since model size and layers stay the same during fine-tuning, and training time just shows effort, not outcome. Accuracy on validation data actually measures if the model got better.

Model size and layers are definitely fixed for a fine-tuned model, so they don’t reflect performance changes. Training duration just tells you how long it took, not if the model got better. Validation accuracy actually measures how well the model generalizes after fine-tuning, which is why it’s the go-to metric. Could there be any scenarios where another metric might be more relevant, like loss or perplexity depending on the task?

B/C? Training duration (C) shows effort spent but doesn’t guarantee better results, whereas accuracy (B) actually reflects performance. So B makes more sense for assessing fine-tuning impact.

Maybe D, if we think about model architecture changes during fine-tuning, but that’s not typical. More likely B because training duration or model size don’t really reflect if the fine-tuning actually improved the model’s predictions. Accuracy on a validation set directly measures how well the model handles new data after fine-tuning, which is usually the main goal. So, B feels like the most practical and commonly used metric here.

Model size and number of layers don’t really tell us if the fine-tuning worked or not. Accuracy on a validation set is a direct way to see if performance improved, so it seems like the best fit here. Could training duration ever reflect efficiency instead?

Makes sense to focus on how well the model generalizes after fine-tuning, so measuring accuracy on a validation set (B) really shows actual performance.

D imo, model size and number of layers are more about architecture, not performance. Training duration just tells time spent, not how well the model actually works.

B imo, accuracy on validation set seems most relevant here.