NCA-GENL Core Machine Learning and AI Knowledge Practice Question
Which component of an NVIDIA AI stack is primarily responsible for providing a low-level API for high-performance collective communication primitives across multi-GPU nodes?
⚠ Common exam trap
Test-takers often confuse NCCL with high-level training frameworks or general container runtimes like CUDA, missing its specific role as the low-level library for multi-GPU communication primitives.
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
✓
NCCL
NCCL (NVIDIA Collective Communications Library) is the industry standard for multi-GPU communication. It is designed to provide high-performance primitives such as All-Reduce, All-Gather, and Broadcast, which are essential for distributed training and inference. Understanding NCCL is fundamental for scaling models across nodes, as it directly impacts the efficiency of gradient synchronization and model parallel strategies in high-performance computing environments.
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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cuBLAS
Why it's wrong here
cuBLAS is a GPU-accelerated library for basic linear algebra operations like matrix multiplication. It focuses on local compute performance rather than inter-node or inter-GPU communication protocols. It is used inside the kernel logic but does not manage the synchronization between multiple GPU nodes in a cluster.
- ✓
NCCL
Why this is correct
NCCL provides optimized collective communication routines that are aware of the underlying topology, such as NVLink and InfiniBand. It is the core library used by frameworks like PyTorch and TensorFlow to handle the synchronization of gradients during distributed training, ensuring maximum utilization of high-speed interconnects.
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cuDNN
Why it's wrong here
cuDNN is a library of primitives for deep neural networks, focusing on operations like convolutions, pooling, and normalization. While it is essential for high-performance training, it does not handle the multi-node communication or network-level synchronization required to distribute training tasks across multiple GPUs in a cluster.
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TensorRT
Why it's wrong here
TensorRT is an inference SDK designed to optimize and deploy models by performing graph optimizations and kernel selection. It does not provide the underlying communication primitives required for training synchronization; its primary goal is to minimize inference latency rather than coordinating distributed compute workloads across nodes.
About these practice questions
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JA
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.