NVIDIA-Certified Professional

AI Operations

(NCP-AIOL)

About This Certification
Exam Preparation
Exam Study Guide
Exam Blueprint
Contact Us

About This Certification
Exam Preparation
Exam Study Guide
Exam Blueprint
Contact Us

About This Certification

The NCP-AI Operations certification is an intermediate-level credential that validates a candidate’s ability to monitor, troubleshoot, and optimize AI infrastructure by NVIDIA. The exam is online and proctored remotely, includes 30 multiple choice questions, 3 hands-on lab exercises, and has a 120-minute time limit.

Note: The hands-on lab is completed as part of the 120-minute exam session. Once the exam begins, the lab environment is automatically provisioned, and you must complete both the multiple-choice section and the hands-on lab within the allotted time.

To successfully complete the lab, you should be comfortable using a Linux command-line interface operating on live clusters using Slurm, Kubernetes, and Base Command Manager.

Please carefully review our certification FAQs and exam policies before scheduling your exam.

If you have any questions, please contact us here.

To access the exam, you’ll need to create a Certiverse account.

Certification Exam Details

Duration: 120 minutes

Price: $500

Certification level: Professional

Subject: AI Operations

Number of questions: 30

Hands-on lab: Yes

Scoring: Pass/Fail

Prerequisites: Two to three years of operational experience working in a data center with NVIDIA hardware solutions. The candidate should be able to monitor and manage all the parts of a data center infrastructure in support of AI workloads.

Language: English

Validity: This certification is valid for two years from issuance. Recertification may be achieved by retaking the exam.

Credentials: Upon passing the exam, participants will receive a digital badge and optional certificate indicating the certification level and topic.

Exam Preparation

Topics Covered in the Exam

Topics covered in the exam include:

Base Command Manager for configuration, management, and troubleshooting
Slurm cluster administration
Kubernetes cluster administration
System management tools for troubleshooting and performance optimization

Candidate Audiences

MLOps engineers
DevOps engineers
Solution architects
System architects
AI Infrastructure engineers

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Exam Study Guide

Review Study Guide

Exam Blueprint

The table below provides an overview of the topic areas covered in the certification exam and how much of the exam is focused on that subject.

Topics Areas	% of Exam	Topics Covered
Installation and Deployment	31%	Describe the Mission Control toolkit Use BCM’s Base View interface to monitor cluster performance, resource utilization, and node health in real time. Manage job scheduling and resource allocation using BCM’s workload manager (e.g., SLURM or Kubernetes) Apply patches, update firmware, and synchronize software images across cluster nodes using BCM Administer user accounts, roles, and permissions to ensure secure access to the cluster using BCM Configure and monitor network settings for cluster nodes, DPUs, and switches using BCM Diagnose and resolve cluster issues, such as job failures, node outages, or resource bottlenecks, using BCM. Use BCM to organize and configure compute nodes into categories based on hardware or workload requirements. Using BCM, maintain documentation and generate reports on cluster usage, performance, and issues. Install and initialize Kubernetes on NVIDIA hosts using BCM Deploy DOCA Services on DPU Arm Install Run:ai Install Slurm
Administration	23%	Administer Slurm cluster. Describe data center architecture for AI Workloads Administer Run:ai Administer Kubernetes Configure MIG
Workload Management	23%	Deploy inference workloads with Kubernetes Deploy inference workloads with Run:ai Deploy training workloads with Slurm Deploy training workloads with Run:ai Use system management tools to troubleshoot issues Allocate resources between teams with Run:ai, Slurm and Kubernetes Deploy containers from NGC
Troubleshooting and Optimization	23%	Troubleshoot Docker Troubleshoot the fabric manager service for NVLink and NVSwitch systems Troubleshoot Base Command Manager Troubleshoot Magnum IO components Troubleshoot storage performance Troubleshoot the deployment of a container from NGC

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