Selected NCA-AIIO NVIDIA-Certified Associate Questions Answers

Cloud GPU infrastructure lowers the cost barrier to entry by offering a pay-as-you-go model, eliminating the need for significant upfront capital expenditure on hardware. While on-prem may offer I/O cost savings or hardware control, the cloud’s accessibility and reduced initial investment make it a compelling choice for organizations seeking immediate GPU access without large sunk costs.

(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Cloud GPU Advantages)

The NVIDIA Data Center GPU Manager (DCGM) is the recommended tool for data center administrators to monitor NVIDIA GPUs. It provides real-time health monitoring, telemetry (e.g., utilization, temperature), and diagnostics, tailored for large-scale deployments. NetQ focuses on network monitoring, and there’s no “NVIDIA System Monitor” in this context, making DCGM the correct choice.(Note: The document incorrectly lists D; C is intended.)

(Reference: NVIDIA DCGM Documentation, Overview Section)

DGX OS, the operating system powering NVIDIA DGX systems, is built on Ubuntu Linux, specifically the Long-Term Support (LTS) version. It integrates Ubuntu’s robust base with NVIDIA-specific enhancements, including GPU drivers, tools, and optimizations tailored for AI and high-performance computing workloads. Neither Red Hat nor CentOS serves as the foundation for DGX OS, making Ubuntu the correct choice.

(Reference: NVIDIA DGX OS Documentation, System Requirements Section)

Job scheduling in an AI cluster assigns workloads (e.g., training, inference) to available compute resources (GPUs, CPUs), optimizing resource utilization and ensuring efficient execution. It’s distinct from data analysis, monitoring, or software management, focusing solely on workload distribution.

(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Job Scheduling)