NCA AI & Deep Learning Frameworks 2

Question 1

Which NVIDIA library provides GPU-accelerated primitives specifically optimized for deep neural network operations like convolutions and RNNs?

Accepted Answer

cuDNN

Answer

cuDNN (CUDA Deep Neural Network library) provides highly optimized primitives for deep learning operations including convolutions, pooling, and recurrent layers.

Question 2

In PyTorch, what is the purpose of `torch.no_grad()` context manager?

Accepted Answer

Prevents gradient computation to save memory and speed up inference

Answer

`torch.no_grad()` disables autograd tracking, reducing memory usage and computation during inference when gradients are not needed.

Question 3

What does the NVIDIA CUDA Unified Memory feature allow developers to do?

Accepted Answer

Access a single memory space accessible by both CPU and GPU without explicit data transfers

Answer

Unified Memory creates a managed memory pool accessible by both CPU and GPU, with the CUDA driver handling data migration automatically.

Question 4

Which TensorFlow API is recommended for building models using a layer-by-layer approach with maximum flexibility for custom training loops?

Accepted Answer

tf.keras.Model subclassing

Answer

Subclassing `tf.keras.Model` gives maximum flexibility, allowing custom forward passes and fine-grained control over training logic.

Question 5

In the context of NVIDIA GPUs, what is a 'warp' in CUDA terminology?

Accepted Answer

A group of 32 threads that execute the same instruction simultaneously

Answer

A warp is the fundamental unit of thread scheduling on NVIDIA GPUs, consisting of 32 threads that execute in lockstep (SIMT execution).

(NCA) NVIDIA-Certified Associate Practice Test

(NCA) NVIDIA-Certified Associate Practice Test

NCA AI & Deep Learning Frameworks 2