NCA Computer Vision & NLP Workloads 2 — Questions and Answers
Question 1: Which NVIDIA library provides GPU-accelerated primitives for computer vision preprocessing tasks such as resizing, normalization, and color space conversion?
- NCCL
- DALI (Correct answer)
- cuBLAS
- TensorRT
Correct answer: DALI
NVIDIA DALI (Data Loading Library) provides highly optimized GPU-accelerated data preprocessing pipelines for computer vision workloads.
Question 2: In NLP transformer models, what technique allows processing longer sequences than the model was originally trained on by adjusting positional encodings?
- Quantization-aware training
- Rotary Position Embedding (RoPE) scaling (Correct answer)
- Flash Attention v2
- Knowledge distillation
Correct answer: Rotary Position Embedding (RoPE) scaling
RoPE scaling modifies the rotation frequencies of positional encodings to extend context length beyond the training window.
Question 3: What is the primary purpose of using INT8 quantization for a deployed computer vision model on NVIDIA GPUs?
- To increase model accuracy during training
- To reduce memory footprint and increase inference throughput (Correct answer)
- To enable multi-GPU training with NVLink
- To support dynamic input shapes
Correct answer: To reduce memory footprint and increase inference throughput
INT8 quantization reduces model weights from 32-bit or 16-bit floats to 8-bit integers, cutting memory usage and boosting inference speed with minimal accuracy loss.
Question 4: Which component of TensorRT is responsible for fusing multiple consecutive layers (e.g., Conv + BatchNorm + ReLU) into a single optimized kernel?
- cuDNN backend
- Layer fusion optimizer (Correct answer)
- NCCL communicator
- CUDA graph
Correct answer: Layer fusion optimizer
TensorRT's layer fusion optimizer combines compatible adjacent layers into a single kernel, reducing memory bandwidth and kernel launch overhead.
Question 5: For a real-time object detection pipeline, which NVIDIA framework provides end-to-end video analytics with DeepStream?
- RAPIDS cuML
- Triton Inference Server
- DeepStream SDK (Correct answer)
- NeMo toolkit
Correct answer: DeepStream SDK
NVIDIA DeepStream SDK provides a streaming analytics toolkit built on GStreamer for building AI-powered video analytics applications.
Question 6: When deploying an NLP model with variable-length input sequences, which TensorRT feature enables handling different sequence lengths without recompiling the engine?
- Static shapes
- Dynamic shapes (Correct answer)
- FP16 mode
- Calibration cache
Correct answer: Dynamic shapes
TensorRT dynamic shapes allow engines to accept inputs within a specified min/opt/max shape range without requiring separate compiled engines per length.
Question 7: Which NVIDIA tool can profile a PyTorch computer vision training loop to identify GPU kernel bottlenecks and memory usage patterns?
- NVIDIA Nsight Systems (Correct answer)
- NVIDIA NCCL
- CUDA Math Library
- cuSPARSE
Correct answer: NVIDIA Nsight Systems
Nsight Systems provides system-wide performance analysis including CPU/GPU interaction timelines, kernel durations, and memory transfers.
NCA Computer Vision & NLP Workloads 2
Which NVIDIA library provides GPU-accelerated primitives for computer vision preprocessing tasks such as resizing, normalization, and color space conversion?