NCA AI & Deep Learning Frameworks 3

Question 1

Which NVIDIA tool is used to profile GPU kernel performance and identify bottlenecks in deep learning workloads?

Accepted Answer

Nsight Systems / Nsight Compute

Answer

Nsight Systems provides system-level profiling while Nsight Compute offers detailed GPU kernel analysis for optimizing deep learning workloads.

Question 2

What is the key advantage of using JAX over standard NumPy for deep learning research?

Accepted Answer

Automatic differentiation and JIT compilation via XLA for GPU/TPU acceleration

Answer

JAX provides `grad()` for automatic differentiation and `jit()` for XLA-based JIT compilation, enabling high-performance research on accelerators.

Question 3

In PyTorch's Distributed Data Parallel (DDP), how are gradients synchronized across GPUs?

Accepted Answer

All-reduce operations synchronize gradients after the backward pass

Answer

DDP uses all-reduce (typically via NCCL) to average gradients across all GPUs simultaneously, making it more efficient than parameter server approaches.

Question 4

What does NVIDIA TensorRT primarily optimize when deploying deep learning models?

Accepted Answer

Inference latency and throughput through layer fusion, precision calibration, and kernel auto-tuning

Answer

TensorRT optimizes trained models for deployment by fusing layers, selecting optimal kernels, and applying INT8/FP16 quantization to maximize inference performance.

Question 5

Which framework introduced the concept of 'eager execution' as its default mode, executing operations immediately without building a static graph?

Accepted Answer

TensorFlow 2.x

Answer

TensorFlow 2.x made eager execution the default, allowing operations to run immediately like NumPy, which greatly simplified debugging and experimentation.

(NCA) NVIDIA-Certified Associate Practice Test

(NCA) NVIDIA-Certified Associate Practice Test

NCA AI & Deep Learning Frameworks 3