Why Learn NVIDIA AI Operations in 2026?

The NCP-AI Operations 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 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.

What Skills Do You Gain with the NVIDIA NCP-AIOL ?

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

What Topics Are Covered in the NVIDIA NCP-AIOL Exam?

1. Installation and Deployment:

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. Administration:

Administer Slurm and Kubernetes clusters
Manage AI data center architecture
Configure MIG (Multi-Instance GPU)
Manage users, roles, and permissions

3. Workload Management:

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. Troubleshooting and Optimization:

Troubleshoot Docker and container deployments
Resolve issues in NVLink, NVSwitch, and Magnum IO
Optimize storage and system performance
Diagnose cluster failures and resource bottlenecks

Who Should Take the NVIDIA NCP-AIOL ?

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

Who Should Take the NCP-AIOL ?

MLOps Engineers
DevOps Engineers
AI Infrastructure Engineers
Solution Architects
System Administrators managing AI workloads
Professionals working with GPU clusters and AI platforms

How NCP-AIOL Helps Advance Your AI Infrastructure Career?

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

Why Organizations Need NVIDIA AI Operations Professionals?

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

What Jobs Can You Get After NCP-AIOL ?

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 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.