NCP-GENL GPU Acceleration and Optimization Practice Question
Which hardware component of an NVIDIA GPU is most responsible for accelerating matrix-multiply-accumulate (MMA) operations used in transformer layers?
⚠ Common exam trap
Candidates often confuse general-purpose CUDA cores with specialized Tensor Cores when asked about matrix-multiply-accumulate acceleration.
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
✓
Tensor Cores.
Tensor Cores are specialized hardware units designed to perform high-speed matrix-multiply-accumulate operations in a single clock cycle. By accelerating these core operations, Tensor Cores provide the massive compute throughput needed for deep learning. Understanding the role of Tensor Cores is vital because they define the performance limits for modern LLMs, and optimizing code to utilize them is the single most important task in GPU performance tuning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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CUDA Cores.
Why it's wrong here
CUDA cores are general-purpose streaming processors designed for parallel integer and floating-point operations. While they can perform matrix multiplication, they are significantly slower and less power-efficient for these specific operations compared to the specialized hardware found in Tensor Cores designed for AI workloads.
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Streaming Multiprocessor (SM) Scheduler.
Why it's wrong here
The SM scheduler is responsible for dispatching instructions to execution units within the multiprocessor. It manages the flow of work but does not perform the mathematical computation of the matrix operations itself; it simply orchestrates the process for the functional units like Tensor Cores.
- ✓
Tensor Cores.
Why this is correct
Tensor Cores are specialized hardware units optimized for high-performance matrix-multiply-accumulate operations. They are the engine behind modern generative AI, allowing GPUs to process large transformer models with extreme efficiency, significantly outperforming general-purpose cores for the math-heavy tasks required by LLMs and neural networks.
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L2 Cache controller.
Why it's wrong here
The L2 cache controller manages data flow between VRAM and the SMs. While it is essential for memory performance, it does not perform mathematical computations. It ensures that the Tensor Cores and CUDA cores have a steady supply of data, but it has no role in accelerating calculations.
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Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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