NCA-AIIO Exam Dumps : NVIDIA-Certified Associate AI Infrastructure and Operations

NVIDIA Base Command Manager is an enterprise-grade platform for monitoring, orchestrating, and managing AI infrastructure at scale, including DGX clusters and cloud resources. It offers unified visibility and workflow automation. DCGM focuses on GPU monitoring, DGX Manager is system-specific, and NeMo System Manager is fictional, making Base Command Manager the enterprise solution.

(Reference: NVIDIA Base Command Manager Documentation, Overview Section)

The correct answer is A because memory-transfer bottlenecks are addressed by GPU memory-system features such as high-bandwidth memory, shared memory, cache, and high-bandwidth interconnects or buses. NVIDIA’s Blackwell tuning guide describes the GPU memory system and states that the NVIDIA B200 GPU supports HBM3 and HBM3e high-bandwidth memory with capacity up to 180 GB. NVIDIA’s CUDA tuning documentation also describes shared memory as an important architectural resource available per streaming multiprocessor, which helps reduce slower memory traffic when used effectively.

Why the other options are incorrect: GPUs are not normally wired as main disk controllers. Increasing generic PCIe I/O ports does not directly solve simulation memory-transfer bottlenecks inside GPU execution. Dedicated inference ASICs are not the general NVIDIA GPU architectural feature used to address memory-transfer performance in simulation workloads.

InfiniBand is renowned for three key features: low latency (microsecond-scale communication), high bandwidth (100 Gb/s and beyond), and CPU offloads (via RDMA), which shift data transfer tasks to the network hardware, boosting system efficiency. High latency contradicts InfiniBand’s design, and GPU offloads are not a core networking feature, making low latency, high bandwidth, and CPU offloads the definitive trio.

(Reference: NVIDIA Networking Documentation, Section on InfiniBand Features)