AI Infrastructure & Operations (NCA-AIIO)

Define an AI workload and show how computer vision, natural language processing, large language models, and recommendation systems drive distinct GPU, memory, storage, and networking needs.

Explore how data pipelines move data from source to ai systems, covering generation, ingestion, storage, processing, and delivery for training or inference, and identify bottlenecks and reliability concerns.

Explore how enterprise AI workloads map to real business scenarios, translating healthcare imaging, fraud detection, and personalization into computer vision, recommendation, and NLP/LLM workloads.

Explore how the NVIDIA AI software stack connects GPU hardware to AI workloads, from drivers and CUDA to libraries, frameworks, and applications. Practice bottom-up troubleshooting to avoid exam traps.

Clarify CUDA's role as a software platform that enables GPU computing and massive parallelism for infrastructure professionals. Explain the CUDA driver and frameworks layering, compatibility, and common CUDA-related failures.

Explore why NVIDIA NGC containers simplify AI deployment by packaging CUDA libraries, frameworks, and optimized components, while host drivers and version alignment remain essential.

Learn how to deploy trained models for inference using TensorRT on NVIDIA GPUs, focusing on latency reduction, throughput, and cost-efficient deployment separate from training.

Learn how AI platforms orchestrate GPU resources, workloads, and environments at scale with NVIDIA Base Command, enabling governance, scheduling, fairness, and visibility across enterprise AI operations.

Explore how power, cooling, and rack space constrain AI data centers, as NVIDIA tests these limits and high-density GPUs drive thermal throttling, demanding infrastructure redesign over software tweaks.

Explore scale up and scale out as two AI infrastructure scaling models, and learn how GPU communication, networking, and workload planning guide the exam-specific scaling decisions.

Understand how east-west traffic, the internal server-to-server data movement in AI clusters, governs distributed training, including model parameter synchronization and gradient exchanges, impacting training speed and scalability.

Evaluate Ethernet and InfiniBand for AI training by analyzing latency, bandwidth, and synchronization in distributed clusters to choose the right interconnect for workload scale.

Explore latency concepts in AI training and how RDMA enables direct memory-to-memory communication to reduce CPU overhead, lower latency, and improve GPU synchronization in large high-performance clusters.

Identify networking bottlenecks in AI training by analyzing latency and bandwidth impacts on the compute-to-synchronize-to-compute cycle, scaling efficiency, and symptom patterns that reveal network saturation.

Storage must scale with compute to sustain high throughput during ai training, feeding GPUs with data and preventing idle time caused by data loading delays.

Define throughput and IOPS, and explain why AI training favors throughput while databases emphasize I/O operations. Match storage metrics to workload patterns for exam-ready, context-driven decisions.

Learn how checkpoints save intermediate training progress to enable failure recovery and resume long-running AI model training, and how model artifacts provide deployable outputs.

Learn how GPU direct storage moves data directly from storage to GPU memory, reducing CPU overhead. This improves throughput for large-scale AI training.

Boost AI training efficiency by reducing CPU bottlenecks with direct storage-to-GPU data paths, cutting copies and increasing throughput in large-scale GPU clusters.

Explore how NVIDIA MIG partitions a single GPU into multiple hardware-isolated instances to boost utilization, support multi-tenant workloads, and deliver predictable performance for lightweight workloads.

Understand how virtual GPU, or vGPU, lets a single physical GPU serve multiple virtual machines. Hypervisors allocate resources for flexible, scalable GPU acceleration in cloud and enterprise environments.

Explore multi-tenant gpu infrastructure, comparing bare-metal, mig, and vgpu to balance performance, isolation, and utilization while maximizing gpu cost efficiency for training and inference workloads.

Compare on-premises vs cloud AI infrastructure, weighing hardware and data control against rapid scalability and pay-as-you-go flexibility to match experimentation, long-term workloads, and data sensitivity.

Coordinate AI clusters that link nodes with GPUs, memory, and storage to scale training, inference, and processing. Master scheduling and resource allocation for fairness and efficiency in multi-user workloads.

Compare Slurm and Kubernetes for AI workloads, highlighting Slurm's batch, fixed resources and HPC roots versus Kubernetes' dynamic, continuous orchestration for inference and microservices.

Explore how job scheduling manages when and where jobs run in AI clusters, balancing FIFO, priority-based and backfilling strategies to maximize throughput, utilization and fairness.

Learn how monitoring compares GPU activity with CPU activity to detect bottlenecks, optimize resources, and balance throughput and latency in AI workloads.

Diagnose low gpu utilization by tracing upstream bottlenecks such as storage delays, data loading, cpu constraints, and network or scheduling issues during distributed training.

Master how to pick the right AI metrics by balancing throughput, latency, and utilization. For training, prioritize throughput; for inference, prioritize latency; use system-level reasoning to identify bottleneck.

Coordinate GPUs, CPUs, storage, networks, schedulers, and orchestration platforms to prevent cascading slowdowns. Monitor data pipelines and workloads to identify bottlenecks during scaling, improve utilization, and balance costs with Kubernetes.

Plan ai infrastructure capacity by balancing performance and cost across gpus, cpus, storage, and networking while accounting for workload patterns, training versus inference needs, and scalable strategies.

Learn how AI systems maintain reliability by detecting problems, preventing single points of failure with redundancy and automatic failover, and enabling self-healing through automation.