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Exam NCA-GENM Topic 1 Question 177 Discussion

Actual exam question for NVIDIA's NCA-GENM exam
Question #: 177
Topic #: 1
You are training a multimodal model that combines audio and video dat
a. You observe that the model performs well on the training data but generalizes poorly to unseen data. Which of the following regularization techniques is MOST likely to improve the generalization performance in this scenario?

Suggested Answer: E Vote an answer

Data augmentation is the most effective regularization technique in this scenario because it increases the diversity of the training data, making the model more robust to variations in unseen data L1 and L2 regularization can help prevent over fitting, but data augmentation directly addresses the issue of limited training data. Dropout also helps, but data augmentation is generally more impactful for multimodal data where variations are significant. Early stopping can also help, but it is not as effective as data augmentation.

by Odelia at May 21, 2025, 11:09 AM

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