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NVIDIA Certified Professional - AI Infrastructure (NCP-AII)

Exam Overview

The NVIDIA Certified Professional - AI Infrastructure certification validates expertise in designing, deploying, and managing GPU-accelerated infrastructure for AI workloads. This certification covers NVIDIA DGX systems, GPU clusters, Kubernetes GPU management, job scheduling, and performance tuning for enterprise AI environments.

Exam Code: NCP-AII Exam Duration: 120 minutes Number of Questions: 60-70 questions Exam Format: Multiple choice Cost: $200 USD Validity: 2 years Prerequisites: Recommended experience with data center infrastructure and GPU computing

Exam Domains

Domain 1: DGX Systems and GPU Hardware (20%)

  • NVIDIA DGX platform architecture and configurations
  • GPU hardware specifications (A100, H100, H200, B200)
  • NVLink and NVSwitch interconnects
  • DGX OS and Base Command
  • Hardware monitoring and diagnostics
  • Storage architecture for AI workloads

Domain 2: GPU Cluster Design and Networking (20%)

  • Multi-node GPU cluster architectures
  • InfiniBand and high-speed networking for AI
  • Network topologies for GPU clusters
  • Storage solutions (parallel file systems, NFS, object storage)
  • Cluster sizing and capacity planning
  • Data center considerations (power, cooling)

Domain 3: Kubernetes and GPU Orchestration (20%)

  • NVIDIA GPU Operator for Kubernetes
  • GPU scheduling and resource management
  • Multi-Instance GPU (MIG) configuration
  • Container runtime for GPUs (NVIDIA Container Toolkit)
  • GPU monitoring in Kubernetes environments
  • Node feature discovery and labeling

Domain 4: Job Scheduling and Workload Management (20%)

  • Slurm and job scheduling for GPU clusters
  • NVIDIA Base Command Manager
  • Multi-tenant GPU sharing
  • Priority and preemption policies
  • Resource quotas and fair-share scheduling
  • Job profiling and optimization

Domain 5: Performance Tuning and Optimization (20%)

  • GPU utilization monitoring and profiling
  • Memory optimization for training workloads
  • Multi-GPU training strategies (data parallel, model parallel)
  • Network bandwidth optimization
  • Storage I/O tuning for data pipelines
  • Benchmarking and baseline establishment

Key Study Areas

NVIDIA DGX Platform

  • DGX H100 - 8x H100 GPUs with NVSwitch interconnect
  • DGX SuperPOD - Large-scale GPU cluster reference architecture
  • DGX Cloud - Cloud-hosted DGX infrastructure
  • Base Command - Job management and orchestration platform
  • DGX OS - Optimized Ubuntu-based operating system

GPU Technologies

  • NVLink - High-bandwidth GPU-to-GPU interconnect
  • NVSwitch - Full-bandwidth GPU switching fabric
  • MIG - Multi-Instance GPU for workload partitioning
  • CUDA - GPU programming model and runtime
  • TensorRT - Inference optimization engine

Infrastructure Software

  • NVIDIA GPU Operator - Kubernetes GPU management
  • NVIDIA Container Toolkit - GPU-accelerated containers
  • DCGM - Data Center GPU Manager for monitoring
  • Nsight Systems - Performance profiling tool
  • NCCL - NVIDIA Collective Communications Library

Hands-On Skills Required

Cluster Management

  • Deploying DGX systems and GPU nodes
  • Configuring InfiniBand networking for GPU clusters
  • Managing GPU resources with Kubernetes and Slurm
  • Monitoring GPU health and utilization with DCGM

Performance Optimization

  • Profiling GPU workloads with Nsight Systems
  • Tuning multi-GPU training configurations
  • Optimizing network and storage for AI pipelines
  • Benchmarking cluster performance

Operations

  • Automating GPU node provisioning
  • Implementing multi-tenant resource policies
  • Troubleshooting GPU hardware and software issues
  • Planning capacity for growing AI workloads

Study Tips

  1. Hands-On Practice: Work with GPU Operator, DCGM, and MIG if possible
  2. DGX Architecture: Understand the full DGX hardware stack deeply
  3. Networking: InfiniBand and NVLink are heavily tested
  4. Kubernetes Focus: GPU Operator and scheduling are key exam areas
  5. Performance Profiling: Know DCGM metrics and Nsight tools
  6. Scale Thinking: Always consider multi-node, multi-GPU scenarios
  7. Documentation: Study NVIDIA docs thoroughly
  8. Stay Current: Follow NVIDIA data center product updates

Exam Registration

Register through: - NVIDIA Certification Portal: Online proctored exam via Pearson VUE - Pearson VUE: Testing center locations worldwide

Career Benefits

Job Opportunities

  • AI Infrastructure Engineer
  • GPU Cluster Administrator
  • AI Platform Engineer
  • Data Center Architect
  • HPC Systems Engineer

Professional Development

  • Validates expertise in enterprise AI infrastructure
  • Demonstrates proficiency with NVIDIA data center technologies
  • Foundation for advanced AI systems management roles
  • Industry recognition in GPU-accelerated computing