NCA-GENL Core Machine Learning and AI Knowledge Practice Question
In the context of NVIDIA's Tensor Core architecture, what is the primary purpose of 'Sparsity' support?
⚠ Common exam trap
Candidates often assume sparsity is used solely for reducing model storage size or memory footprint on disk, ignoring its primary hardware execution benefit of doubling computational throughput.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
To double the computational throughput during GEMM operations.
NVIDIA's Fine-Grained Structured Sparsity is a hardware feature that allows the GPU to skip computations for zero-valued weights in neural networks. By enforcing a 2:4 sparsity pattern (where at least 2 out of every 4 consecutive weights are zero), the hardware can double the throughput of matrix multiplication operations. This feature is vital for accelerating LLMs without sacrificing model accuracy, as it effectively doubles compute density on supported modern NVIDIA architectures.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
To reduce the amount of VRAM consumed by model weights.
Why it's wrong here
Sparsity optimization is focused on increasing compute throughput, not primarily on memory compression. While sparse formats can potentially store weights in less space, the primary purpose of the Tensor Core sparsity feature is to accelerate matrix multiplication by ignoring zero-valued computations in the hardware execution pipeline.
- ✓
To double the computational throughput during GEMM operations.
Why this is correct
Structured sparsity enables Tensor Cores to effectively halve the number of required arithmetic operations by skipping zeros. When a matrix satisfies the 2:4 sparsity pattern, the hardware skips calculations for the zero weights, enabling a theoretical 2x speedup in matrix multiplication speed without compromising the model's predictive accuracy.
- ✗
To automatically prune the model during the training process.
Why it's wrong here
Hardware sparsity is a deployment-level optimization; it does not perform the pruning itself. The model must be trained or fine-tuned to comply with the 2:4 sparsity pattern before it can be deployed on hardware that utilizes this feature. The hardware simply accelerates the execution of an already sparse model.
- ✗
To allow models to run without any normalization layers.
Why it's wrong here
Sparsity support has no relationship with normalization layers like LayerNorm or BatchNorm. Normalization is a mathematical technique for training stability, whereas hardware sparsity is a structural optimization applied to the weight matrices to enhance the raw computational performance of the GPU during the inference or training of large models.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
This NCA-GENL practice question is part of Courseiva's free NVIDIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the NCA-GENL exam.