Free NVIDIA NCA-GENL Practice Exam with Questions & Answers | Set: 2

Quantization in deep learning involves reducing the precision of model weights and activations (e.g., from 32-bit floating-point to 8-bit integers) to optimize performance. According to NVIDIA’s documentation on model optimization and deployment (e.g., TensorRT and Triton Inference Server), quantization offers several benefits:

Option A: Quantization reduces power consumption and heat production by lowering the computational intensity of operations, making it ideal for edge devices.

Image transformation techniques such as flipping, rotation, and zooming are forms of data augmentation used to artificially increase the size and diversity of a dataset. NVIDIA’s Deep Learning AI documentation, particularly for computer vision tasks using frameworks like DALI (Data Loading Library), explains that data augmentation improves a model’s ability to generalize by exposing it to varied versions of the training data, thus reducing overfitting. For example, flipping an image horizontally creates a new training sample that helps the model learn invariance to certain transformations. Option A is incorrect because transformations do not simplify the model architecture. Option C is wrong, as augmentation introduces variability, not uniformity. Option D is also incorrect, as augmentation typically increases computational requirements due to additional data processing.

Regular expression (regex) filters are widely used in data preprocessing to select text data containing specific keywords or patterns. NVIDIA’s documentation on data preprocessing for NLP tasks, such as in NeMo, highlights regex as a standard tool for filtering datasets based on textual criteria, enabling efficient data curation. For example, a regex pattern like .*keyword.* can select all texts containing “keyword.” Option A (ActionScript) is a programming language for multimedia, not data filtering. Option B (Tableau) is for visualization, not text filtering. Option C (JSON parser) is for structured data, not keyword-based text selection.

NVIDIA RAPIDS cuDF is a GPU-accelerated library that mimics Pandas’ API but performs data manipulation on GPUs, significantly speeding up preprocessing tasks for LLM fine-tuning. The key feature enabling this performance is GPU-accelerated columnar data processing with zero-copy memory access, which allows cuDF to leverage the parallel processing power of GPUs and avoid unnecessary data transfers between CPU and GPU memory. According to NVIDIA’s RAPIDS documentation, cuDF’s columnar format and CUDA-based operations enable orders-of-magnitude faster data operations (e.g., filtering, grouping) compared to CPU-based Pandas. Option A is incorrect, as cuDF uses GPUs, not CPUs. Option C is false, as cloud integration is not a core cuDF feature. Option D is wrong, as cuDF does not rely on SQL tables.

The HuggingFace Transformers library is widely used for fine-tuning large language models (LLMs) due to its seamless integration with PyTorch and NVIDIA’s TensorRT, enabling GPU-accelerated training and inference. NVIDIA’s NeMo documentation references HuggingFace Transformers for its compatibility with CUDA and TensorRT, which optimize model performance on NVIDIA GPUs through features like mixed-precision training and dynamic shape inference. This makes it ideal for scaling LLM fine-tuning on GPU clusters. Option A is incorrect, as Transformers focuses on GPU, not CPU, pipelines. Option C is partially true but not the primary feature for fine-tuning. Option D is false, as Transformers is for deep learning, not classical algorithms.