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NVIDIA-Certified Professional

AI Infrastructure

(NCP-AII)

Register for Exam

About This Certification
Exam Preparation
Exam Blueprint
Contact Us
Stay Informed

About This Certification

About This Certification
Exam Preparation
Exam Blueprint
Contact Us
Stay Informed

Register for Exam

About This Certification

The NCP-AI Infrastructure certification is an intermediate-level credential that validates a candidate’s ability to deploy, manage, and maintain AI infrastructure by NVIDIA. The exam is online and proctored remotely, includes 65 questions, and has a 90-minute time limit.

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

If you have any questions, please contact us here.

Certification Exam Details

Duration: 90 minutes

Price: $400

Certification level: Professional

Subject: AI Infrastructure

Number of questions: 60-70

Prerequisites: Two to three years of operational experience working in a data center with NVIDIA hardware solutions. The candidate should be able to deploy 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:

Install and configure servers & networks
Physical layer management
Troubleshoot and optimize systems and networks

Candidate Audiences

Data center administrators
Infrastructure administrators
Network administrators
Network engineers
Storage administrators
System administrators
Solution architects

Recommended Training

AI Infrastructure & Operations Fundamentals

A self-paced course that covers essential components of AI infrastructure, including compute platforms, networking, and storage solutions. The course also addresses AI operations, focusing on infrastructure management and cluster orchestration.

AI Infrastructure Professional Workshop

A multi-day workshop that covers the essential aspects of AI infrastructure in modern data centers, focusing on NVIDIA's cutting-edge technologies. The course provides a deep dive into optimizing AI workloads, managing GPU resources, and leveraging NVIDIA's ecosystem to build and maintain efficient AI-driven data centers.

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
Troubleshoot and Optimize	15%	Identify and troubleshoot hardware faults (e.g., GPU, fan, network card) Identify faulty cards, GPUs, power supplies Replace faulty cards, GPUs, power supplies Optimize AMD and Intel servers for performance Optimize storage
Systems and Network	17%	Configure routing tables on InfiniBand and NVIDIA Spectrum-X™ Install and configure NVIDIA NVLink™ Switch Set up network fabric ports for the hosts Identify network topologies for data centers
Systems and Servers	33%	Install GPU-based servers Install physical GPUs Install NVIDIA® Bluefield® DPU-based servers Identify cable types and transceivers Validate hardware operation for workloads Validate hardware installation Validate power and cooling Establish storage requirements in a cluster design
Physical Layer Management	35%	Install, update, and remove NVIDIA GPU drivers Install the NVIDIA Container Toolkit Demonstrate how to use NVIDIA GPUs with Docker Install NGC command line interface on hosts Configure and manage Bluefield Configure MIG (AI and HPC) Deploy the Bluefield OS image to Arm Manage cloud-native stack

Contact Us

NVIDIA offers training and certification for professionals looking to enhance their skills and knowledge in the field of AI, accelerated computing, data science, advanced networking, graphics, simulation, and more.

Contact us to learn how we can help you achieve your goals.

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