NVIDIA-Certified Professional – AI Operations (NCP-AIOL)

The NCP-AI Operations certification focuses on building expertise in AI infrastructure management, cluster orchestration, and performance optimization. Candidates will gain practical knowledge of managing large-scale AI environments using tools such as Slurm, Kubernetes, and NVIDIA Base Command Manager (BCM).

The certification emphasizes real-world operational scenarios, including deploying workloads, managing clusters, troubleshooting system issues, and optimizing performance across GPU-accelerated environments. The exam includes both multiple-choice questions and hands-on lab exercises, ensuring candidates can apply concepts in live environments.

• Monitor and manage AI infrastructure in production environments

• Administer GPU clusters using Slurm and Kubernetes

• Configure and manage workloads across distributed systems

• Troubleshoot performance issues in AI workloads and infrastructure

• Optimize resource utilization for AI training and inference

• Work with NVIDIA tools like Base Command Manager and Run:AI

• Manage containerized AI applications and deployments

• Understand networking and storage for AI data centers

1. AI Cluster Installation and Deployment Skills(31%)

• Configure and manage clusters using Base Command Manager (BCM)

• Deploy Kubernetes, Slurm, and Run:AI environments

• Manage firmware, patches, and system updates

• Configure networking for nodes, DPUs, and switches

2. AI Infrastructure Administration and GPU Management(23%)

• Administer Slurm and Kubernetes clusters

• Manage AI data center architecture

• Configure MIG (Multi-Instance GPU)

• Manage users, roles, and permissions

3. AI Workload Management with Kubernetes and Slurm(23%)

• Deploy AI training workloads using Slurm

• Deploy inference workloads using Kubernetes and Run:AI

• Allocate resources across teams and workloads

• Use system tools for performance monitoring

4. AI Infrastructure Troubleshooting and Performance Optimization(23%)

• Troubleshoot Docker and container deployments

• Resolve issues in NVLink, NVSwitch, and Magnum IO

• Optimize storage and system performance

• Diagnose cluster failures and resource bottlenecks

• 2–3 years of experience managing data center infrastructure

• Hands-on experience with Linux command-line environments

• Familiarity with Kubernetes, Slurm, or cluster orchestration tools

• Understanding of GPU-based computing and AI workloads

• Experience with system monitoring and troubleshooting

• Validates expertise in AI infrastructure and operations

• Positions you as a specialist in GPU and AI workload management

• Enhances credibility in MLOps and AI engineering roles

• Opens opportunities in high-demand AI and data center roles

• Demonstrates hands-on capability with enterprise-grade AI systems

• Improved efficiency in managing AI infrastructure

• Faster troubleshooting and reduced downtime

• Optimized resource utilization and cost savings

• Stronger operational reliability for AI workloads

• Better scalability for AI-driven applications

• AI Infrastructure Engineer

• MLOps Engineer

• DevOps Engineer (AI/ML Focus)

• HPC (High Performance Computing) Engineer

• Cloud & AI Solutions Architect

• Data Center Operations Engineer

The NCP-AIOL certification positions you at the intersection of AI, cloud, and high-performance computing, where demand for skilled professionals is rapidly growing. As organizations continue to scale AI-driven solutions, professionals who can efficiently manage and optimize AI infrastructure will play a critical role.

For more information, refer to this - NVIDIA - [NCP- AIOL]

• Prior Certification: Not Required

• Exam Validity: 2 Years

• Exam Fee: $500 USD

• Exam Duration: 120 Minutes

• Number of Questions: 30 Questions + Hands-on Labs

• Passing Score: 70%

• Recommended Experience: 2–3 years of operational experience working with NVIDIA hardware solutions in data center environments.

• Exam Format: Multiple-choice questions and hands-on labs using Linux command-line interfaces on live clusters with Slurm, Kubernetes, and Base Command Manager.

• Exam Language: English