NVIDIA-Certified Associate

AI Infrastructure and Operations

(NCA-AIIO)

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    About This Certification

    The NCA-AIIO certification is an entry-level credential that validates the foundational concepts of AI computing related to infrastructure and operations. The exam is online and proctored remotely, includes 50 questions, and has a 60-minute time limit.

    Please carefully review NVIDIA’s examination policy before scheduling your exam.

    If you have any questions, please contact us here.

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

    Certification Exam Details

    Duration: One hour

    Price: $125 

    Certification level: Associate  

    Subject: AI Infrastructure and operations  

    Number of questions: 50

    Prerequisites: A basic understanding of data center infrastructure.

    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

    • Accelerated computing use cases
    • AI, machine learning, and deep learning
    • GPU architecture
    • NVIDIA’s software suite
    • Infrastructure and operation considerations for adopting NVIDIA solutions

    Candidate Audiences

    • Business-line owners
    • Data center technicians
    • Delivery and professional services engineers
    • Developer operations (DevOps) engineers
    • IT managers
    • Networking engineers
    • Sales representatives
    • Systems administration staff
    • Solution architects
    • System architects

    Recommended Training

    AI Infrastructure and Operations Fundamentals

    Designed for enterprise professionals, this self-paced course provides invaluable insights into the evolving landscape of AI. Whether you’re a seasoned professional or just beginning your journey into AI, this course is essential for staying ahead in today’s rapidly evolving technological landscape. 

    The typical time to complete this course is seven hours.

    Learn More

    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.

    Topic Areas % of Exam Topics Covered
    Essential AI Knowledge: 38%
    • Describe the NVIDIA software stack used in an AI environment.
    • Compare and contrast training and inference architecture requirements and considerations.
    • Differentiate the concepts of AI, machine learning, and deep learning.
    • Explain the factors contributing to recent rapid improvements and adoption of AI.
    • Explain the key AI use cases and industries.
    • Explain the purpose and use case of various NVIDIA solutions.
    • Describe the software components related to the life cycle of AI development and deployment.
    • Compare and contrast GPU and CPU architectures.
    AI Infrastructure 40%
    • Identify hardware requirements for specific AI training task use cases
    • Scale a GPU infrastructure for different use cases
    • Identify key concepts, and high-level specifications related to power and cooling requirements within a datacenter
    • Articulate the key advantages, challenges, and considerations related to on-prem vs cloud infrastructures
    • Identify key components and considerations of a cluster of an accelerated infrastructure
    • Identify facility requirements
    • Determine networking requirements for AI workloads
    • Identify and describe DC networking protocols and key concepts
    • Identify high speed DC network options and their use cases
    • Explain the purpose and benefits of a DPU in a datacenter
    AI Operations 22%
    • Describe AI data center management and monitoring essentials.
    • Describe AI cluster orchestration and job scheduling essentials.
    • Articulate the key measures and criteria related to monitoring GPUs.
    • Identify the key considerations for virtualizing accelerated infrastructure.

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