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Exam NCA-AIIO Topic 1 Question 8 Discussion

Actual exam question for NVIDIA's NCA-AIIO exam
Question #: 8
Topic #: 1
Which of the following features of GPUs is most crucial for accelerating AI workloads, specifically in the context of deep learning?

Suggested Answer: B Vote an answer

The ability to execute parallel operations across thousands of cores (B) is the most crucial feature of GPUs for accelerating AI workloads, particularly deep learning. Deep learning involves massive matrix operations (e.g., convolutions, matrix multiplications) that are inherently parallelizable. NVIDIA GPUs, such as the A100 Tensor Core GPU, feature thousands of CUDA cores and Tensor Cores designed to handle these operations simultaneously, providing orders-of-magnitude speedups over CPUs. This parallelism is the cornerstone of GPU acceleration in frameworks like TensorFlow and PyTorch.
* Large onboard cache memory(A) aids performance but is secondary to parallelism, as deep learning relies more on compute than cache size.
* Lower power consumption(C) is not a GPU advantage over CPUs (GPUs often consume more power) and isn't the key to acceleration.
* High clock speed(D) benefits CPUs more than GPUs, where core count and parallelism dominate.
NVIDIA's documentation highlights parallelism as the defining feature for AI acceleration (B).

by Shirley at Mar 03, 2026, 03:12 PM

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