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Exam Code:	NCA-AIIO
Exam Name:	NVIDIA-Certified Associate AI Infrastructure and Operations
Certification Provider:	NVIDIA
Free Question Number:	89
Version:	v2025-07-11
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# of Questions views:	7215
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AS - Jan 03, 2026

No.# B. Apply data sharding across multiple CPUs
distributing the I/O and preprocessing load to prevent a single CPU bottleneck from starving the GPU

AS - Jan 03, 2026

No.# C. Optimize the data loading pipeline to ensure continuous GPU data feeding during backpropagation.

In distributed setups, backpropagation is followed immediately by gradient synchronization (all-reduce). If the data loader isn't "feeding" the next batch while the GPU is doing the math/sync, the overall utilization drops

AS - Jan 01, 2026

No.# B. NVIDIA NGC and D. NVIDIA NCCL.
Explanation
B. NVIDIA NGC (NVIDIA GPU Cloud): In 2026, NGC serves as the essential hub for GPU-optimized software. It provides the enterprise with optimized containers (including pre-configured frameworks like PyTorch and TensorFlow) and Helm charts that are specifically tuned to maximize hardware utilization. Using NGC containers ensures that all libraries (CUDA, cuDNN, drivers) are perfectly matched to extract the highest performance from the GPUs without manual configuration overhead. [2]
D. NVIDIA NCCL (NVIDIA Collective Communications Library): This is the critical component for distributed workload distribution. NCCL provides the high-performance communication primitives (like All-Reduce and All-Gather) required to synchronize gradients across multiple GPUs and nodes. It is specifically designed to be topology-aware, meaning it automatically optimizes data paths over NVLink and InfiniBand to eliminate communication bottlenecks, which is the primary factor in maximizing utilization in multi-GPU setups...

AS - Jan 01, 2026

No.# C. NVIDIA Triton Inference Server and GPUDirect RDMA.
Explanation
NVIDIA Triton Inference Server: This component addresses scalability and high availability. Triton is a high-performance inference serving software that can manage multiple models simultaneously on a single or multiple GPUs. It supports dynamic batching, concurrent model execution, and integrates with Kubernetes for orchestration, making it highly scalable and fault-tolerant for a production environment.
GPUDirect RDMA (Remote Direct Memory Access): This technology minimizes latency by allowing direct memory access between GPUs in different servers or between GPUs and networking interfaces, bypassing the CPU. This significantly reduces communication overhead and latency, which is critical for real-time performance in large-scale, distributed systems.

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