Free NVIDIA NCA-AIIO Practice Exam with Questions & Answers

A GPU’s architecture is designed for massive parallelism, featuring thousands of lightweight cores that execute simple instructions across vast data elements simultaneously—ideal for tasks like AI training. In contrast, a CPU has fewer, complex cores optimized for sequential execution and branching logic. GPUs don’t function as PCIe controllers (a hardware role), nor are they single-core designs, making the parallel execution focus the key differentiator.

(Reference: NVIDIA GPU Architecture Whitepaper, Section on GPU Design Principles)

NVIDIA GPUDirect RDMA allows network adapters to directly access GPU memory, bypassing the CPU and operating system kernel. This accelerates data transfers between GPUs and CPUs (or other devices), reducing latency and CPU overhead in AI workflows, such as multi-node training. It doesn’t focus on power efficiency or unsynchronized memory sharing, making faster transfers its key benefit.

(Reference: NVIDIA GPUDirect RDMA Documentation, Overview Section)

OpenSM (Open Subnet Manager) is essential for InfiniBand networks, managing the fabric by discovering topology, configuring switches and host channel adapters (HCAs), and handling routing. Without it, the fabric cannot operate. Verbs is an API for RDMA, and MPI is a communication protocol, but OpenSM is the critical software component for functionality.

(Reference: NVIDIA Networking Documentation, Section on InfiniBand Subnet Management)

The nvidia-smi (System Management Interface) command is the primary tool for checking GPU utilization on NVIDIA systems, including the DGX H100. It provides real-time metrics like utilization percentage, memory usage, and power draw. NVML (NVIDIA Management Library) is an API, not a command, and ctop is unrelated, solidifying nvidia-smi as the standard.

(Reference: NVIDIA DGX H100 System Documentation, Monitoring Section)

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)

NVIDIA Data Center GPU Manager (DCGM) aids data center monitoring and management by providing detailed GPU telemetry, health diagnostics, and performance tracking at scale. Clara targets healthcare, TensorRT optimizes inference, and Mellanox Insight isn’t a standard NVIDIA tool, making DCGM the go-to solution.

(Reference: NVIDIA DCGM Documentation, Overview Section)

On-premises infrastructure offers a key advantage in ensuring data security and sovereignty, as organizations retain direct control over hardware and data, facilitating compliance with strict regulations (e.g., GDPR). Cloud solutions excel in scalability and lower upfront costs, but on-prem provides unmatched authority over sensitive data, outweighing remote management ease in security-critical scenarios.

(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on On-Prem vs. Cloud Infrastructure)

Kubernetes is the industry-standard tool for container orchestration in AI clusters, automating deployment, scaling, and management of containerized workloads. Slurm manages job scheduling, Apptainer (formerly Singularity) runs containers, and MLOps is a practice, not a tool, making Kubernetes the clear leader in this domain.

(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Container Orchestration)

The DGX A100 system includes eight Mellanox ConnectX-6 Single Port VPI cards, providing high-speed connectivity (up to 200 Gb/s) for clustering and data transfer. These cards support versatile protocols (InfiniBand or Ethernet), enabling robust multi-node AI workloads, with eight being the standard configuration for this system.

(Reference: NVIDIA DGX A100 System Documentation, Networking Section)

Deep learning breakthroughs stem from three pillars: advances in hardware (e.g., GPUs and TPUs) providing the compute power for large-scale neural networks; the availability of large datasets offering the data volume needed for training; and improvements in training algorithms (e.g., optimizers like Adam, novel architectures like Transformers) enhancing model efficiency and accuracy. While internet speed, sensors, or smartphones play roles in broader tech, they’re less directly tied to deep learning’s core advancements.

(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Deep Learning Advancements)