What Skills Will You Gain with NCP-AII?

Deploy and configure NVIDIA AI infrastructure environments
Install and validate GPU-based servers and networking components
Configure and manage AI clusters using NVIDIA Base Command Manager (BCM)
Perform firmware upgrades, hardware validation, and fault detection
Configure storage, networking, and GPU infrastructure for AI workloads
Validate AI infrastructure performance using NCCL, HPL, and ClusterKit
Configure NVIDIA GPU environments with Docker and container toolkits
Troubleshoot hardware, networking, storage, and cluster-related issues
Optimize AI infrastructure performance across GPU-enabled environments
Understand AI factory architectures, networking topologies, and infrastructure best practices

What Skills/Topics are Assessed in the NCP-AII Exam?

1. System and Server Bring-up (31%)

Deploy and validate AI infrastructure environments
Configure BMC, OOB, and TPM settings
Perform firmware upgrades and hardware validation
Validate power, cooling, and server installation
Configure storage and networking infrastructure
Install and validate physical GPUs

2. Physical Layer Management (5%)

Configure and manage BlueField® network platforms
Configure Multi-Instance GPU (MIG) environments
Manage physical infrastructure components

3. Control Plane Installation and Configuration (19%)

Install Base Command Manager (BCM) and configure HA
Install operating systems and cluster environments
Configure Slurm, Enroot, and Pyxis
Install NVIDIA GPU and DOCA™ drivers
Configure NVIDIA container toolkit and Docker GPU environments
Install and use NGC™ CLI

4. Cluster Test and Verification (33%)

Perform stress testing and validation workflows
Execute HPL and NCCL validation tests
Verify NVLink™ and networking configurations
Validate cabling and signal quality
Perform ClusterKit assessments and infrastructure burn-in testing
Test storage and cluster performance

5. Troubleshoot and Optimize (12%)

Identify and troubleshoot hardware failures
Replace faulty GPUs, power supplies, and network components
Optimize AMD and Intel server performance
Optimize storage and infrastructure efficiency

Recommended Experience for NCP-AII

2–3 years of experience working with AI infrastructure or data center environments
Familiarity with Linux system administration and networking
Hands-on experience with GPU-based servers and AI workloads
Understanding of storage, networking, and cluster infrastructure
Experience with infrastructure deployment, troubleshooting, and optimization

Who Should Take the NCP-AII Certification?

Data Center Administrators
Infrastructure Administrators
Network Engineers
Storage Administrators
System Administrators
AI Infrastructure Engineers
Solution Architects
Professionals managing GPU-powered AI environments

Benefits of NCP-AII for Your Career

Validates expertise in deploying and managing AI infrastructure
Demonstrates hands-on knowledge of NVIDIA AI ecosystems
Enhances credibility in AI infrastructure and HPC roles
Opens opportunities in GPU, AI, and data center engineering careers
Strengthens practical infrastructure deployment and troubleshooting skills
Demonstrates operational readiness for enterprise AI environments

Benefits for Organizations with NCP-AII Certified Professionals

Faster deployment of AI infrastructure environments
Improved reliability and performance of GPU-based systems
Reduced downtime through better troubleshooting expertise
Optimized AI infrastructure performance and scalability
Stronger operational readiness for enterprise AI workloads
Improved governance and validation of AI data center infrastructure

Career Opportunities with NCP-AII Certification

AI Infrastructure Engineer
Data Center Engineer
HPC (High Performance Computing) Engineer
Infrastructure Architect
GPU Systems Engineer
Network Infrastructure Engineer
AI Platform Administrator
Cloud & AI Infrastructure Architect

The NCP-AII certification positions professionals at the center of modern AI infrastructure deployment and high-performance computing environments. As organizations continue scaling AI workloads, the demand for professionals capable of deploying, validating, and optimizing GPU-powered infrastructure continues to grow rapidly.