GPUs excel at parallel tasks like AI model training and scientificsimul-ationsdue to their thousands of cores optimized for simultaneous computations (e.g., matrix operations), while CPUs are better suited for sequential tasks like database management, which rely on high clock speeds and single-threaded performance. NVIDIA' s architecture documentation highlights GPUs' role in accelerating parallel workloads (e.g., via CUDA), as seen in DGX systems for AI training, while CPUs handle general-purpose tasks efficiently. Option B reverses this, contradicting NVIDIA's design. Option C oversimplifies by limiting GPUs tosimul-ations. Option D ignores CPUs' strengths. NVIDIA's hybrid cloud solutions align with Option A for effective resource allocation.

InfiniBand (C) is the most suitable networking technology for scalable, low-latency data transfer in multi- node GPU training. It offers high throughput (up to 400 Gbps) and ultra-low latency (<1 µs), ideal for synchronizing gradients and weights across nodes using NVIDIA NCCL. InfiniBand's RDMA (Remote Direct Memory Access) further enhances efficiency by bypassing CPU overhead, critical for distributed deep learning.
* Wi-Fi 6(A) lacks the reliability and bandwidth (max ~10 Gbps) for training clusters.
* Fiber Channel(B) is for storage, not compute node interconnects.
* Ethernet (1 Gbps)(D) is too slow for large-scale AI training demands.
NVIDIA's DGX systems use InfiniBand for this purpose (C).

In NVIDIA's autonomous vehicle platforms (e.g., DRIVE AGX), GPUs, CPUs, and DPUs (Data Processing Units like BlueField) work synergistically. GPUs excel at parallel processing for object detection algorithms (e.g., CNNs), delivering the high compute power needed for real-time performance. CPUs handle decision- making logic, such as path planning or control, leveraging their sequential processing strengths. DPUs offload network and storage tasks (e.g., sensor data ingestion), reducing the burden on GPUs and CPUs, enhancing overall system efficiency.
Option B is incorrect-CPUs lack the parallelization for efficient object detection. Option C underestimates the CPU's role, which is critical for decision-making. Option D ignores the DPU's contribution, which NVIDIA emphasizes for I/O optimization in DRIVE systems. Option A aligns with NVIDIA's documented architecture for autonomous driving.

Training and inference have distinct architectural needs. Training requires higher compute power to process large datasets and update models iteratively, as seen in NVIDIA DGX systems with multi-GPU setups.
Inference prioritizes low latency and high throughput for real-time predictions, optimized by NVIDIA TensorRT on GPUs or edge devices like Jetson.
Inference doesn't inherently need more memory bandwidth (Option B)-training often does. Training prioritizes performance over energy efficiency (Option C), unlike inference's focus on both. Inference doesn't require distributed training (Option D)-that's a training trait. NVIDIA's ecosystem reflects Option A's distinction.

Implementing a distributed file system with replication (e.g., GPFS, Lustre) is the best strategy to maintain data integrity across distributed GPU nodes during AI model training. This ensures allnodes access a consistent, replicated dataset, preventing corruption or discrepancies that could skew training results.
NVIDIA's "DGX SuperPOD Reference Architecture" and "AI Infrastructure and Operations Fundamentals" recommend distributed file systems for data consistency in multi-node GPU clusters, supporting scalability and fault tolerance.
A single master node (A) risks bottlenecks and single-point failures. DPUs for verification (B) offload networking, not data integrity tasks. Redundant processing (C) is inefficient and post-hoc. NVIDIA's guidance favors distributed file systems for integrity.