Exam NCA-GENL Topic 10 Question 5 Discussion

Actual exam question for NVIDIA's NCA-GENL exam
Question #: 5
Topic #: 10

In neural networks, the vanishing gradient problem refers to what problem or issue?

A. The problem of overfitting in neural networks, where the model performs well on the trainingdata but poorly on new, unseen data. B. The issue of gradients becoming too large during backpropagation, leading to unstable training. C. The problem of underfitting in neural networks, where the model fails to capture the underlying patterns in the data. D. The issue of gradients becoming too small during backpropagation, resulting in slow convergence or stagnation of the training process.

Suggested Answer: D Vote an answer

The vanishing gradient problem occurs in deep neural networks when gradients become too small during backpropagation, causing slow convergence or stagnation in training, particularly in deeper layers. NVIDIA's documentation on deep learning fundamentals, such as in CUDA and cuDNN guides, explains that this issue is common in architectures like RNNs or deep feedforward networks with certain activation functions (e.g., sigmoid). Techniques like ReLU activation, batch normalization, or residual connections (used in transformers) mitigate this problem. Option A (overfitting) is unrelated to gradients. Option B describes the exploding gradient problem, not vanishing gradients. Option C (underfitting) is a performance issue, not a gradient-related problem.
References:
NVIDIA CUDA Documentation: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html Goodfellow, I., et al. (2016). "Deep Learning." MIT Press.

by Fitch at Jul 25, 2025, 04:18 AM

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