Exam NCA-GENM Topic 1 Question 339 Discussion

Actual exam question for NVIDIA's NCA-GENM exam
Question #: 339
Topic #: 1

You are fine-tuning a pre-trained multimodal model for a new task. You have limited computational resources. Which of the following fine-tuning strategies would be the MOST computationally efficient while still achieving good performance?

A. Fine-tune all the layers of the model. B. Freeze all layers except the classification head and fine-tune only the classification head. C. Freeze the lower layers of the model and fine-tune the upper layers and the classification head. D. Train a new random model from scratch for the task, which will avoid the need to load the pre-trained model. E. Randomize the model to train, if it improves the training rate.

Suggested Answer: C Vote an answer

Freezing the lower layers and fine-tuning the upper layers and classification head strikes a balance between computational efficiency and performance. The lower layers typically capture more general features that are less specific to the task, while the upper layers capture more task-specific features. Freezing the lower layers reduces the number of trainable parameters, making the fine-tuning process more computationally efficient. Fine-tuning all layers is computationally expensive, freezing all layers except the classification head might not be sufficient for adapting to the new task, and training from scratch does not leverage the knowledge learned during pre-training. Randomizing model is not a general practice.

by Elliot at Mar 22, 2025, 03:52 PM

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