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

Training architectures are designed to maximize computational throughput and accelerate model convergence, often by leveraging distributed systems with multiple GPUs or specialized accelerators to process large datasets efficiently. This focus on performance ensures that models can be trained quickly and effectively. In contrast, inference architectures prioritize minimizing response latency to deliver real-time or near-real-time predictions, frequently employing techniques such as model optimization (e.g., pruning, quantization), batching strategies, and deployment on edge devices or optimized servers. These differing priorities mean that while there may be some overlap, the architectures are tailored to their specific goals—performance for training and low latency for inference.

(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Infrastructure Considerations for AI Workloads; NVIDIA Documentation on Training and Inference Optimization)

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)