NVIDIA-Certified Associate

AI Infrastructure and Operations

(NCA-AIIO)

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What is the NVIDIA AI Infrastructure and Operations Certification (NCA-AIIO)?

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 our certification FAQs and exam policies before scheduling your exam.

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

If you have any questions, please contact us.

Exam Details for NCA-AIIO

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.

Who is AI Infrastructure and Operations Certification for?

The NCA-AIIO certification is an entry-level credential that validates foundational knowledge of AI computing infrastructure and operations. Designed for IT professionals new to this field, it is relevant to a broad range of roles, from technical pre-sales to data center operations.

The exam assesses understanding of the components and requirements for adopting AI in enterprise data centers and on-premises environments. Candidates should understand enterprise AI and machine learning concepts, describe essential AI operations tasks, and identify the hardware, software, and networking needed for AI deployments. They should also be familiar with NVIDIA solutions used in these environments.

What are the Recommended Qualifications and Experience for This Exam?

  • Bachelor’s degree in computer science, software engineering, AI, or a related field
  • Experience in enterprise-level data centers and on-prem compute environments .
  • Solid understanding of AI and machine learning concepts

Certification Learning Path

Here is the recommended training to prepare for this certification exam. 

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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

NCA-AIIO Exam Preparation Topics and Recommended Reading

The information below is intended to help you prepare for this exam.
Topics covered, weight per topic, recommended training, and additional readings are provided. 

Essential AI Knowledge

This section focuses on the NVIDIA AI ecosystem, covering its software stack, core AI, ML, and DL concepts, architecture comparisons (GPU/CPU, training/inference), and AI adoption and use cases.

Essential AI Knowledge Potential Exam Topics:

  • 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.

38%
Exam Weight

Recommended Training

Suggested Reading

AI Infrastructure

This section focuses on identifying hardware, scaling GPU infrastructure, understanding power/cooling, comparing on premises vs. cloud, identifying cluster components, facility needs, networking for AI, DC protocols, high-speed network options, and DPU benefits in a data center.

AI Infrastructure Potential Exam Topics:

  • 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 data center.
  • 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 data center networking protocols and key concepts.
  • Identify high-speed data center network options and their use cases.
  • Explain the purpose and benefits of a DPU in a data center.

40%
Exam Weight

Recommended Training

Suggested Reading

AI Operations

This section focuses on the essential skills for managing and monitoring AI data centers, cluster orchestration and job scheduling, GPU monitoring metrics, and virtualization considerations for accelerated AI infrastructure.

AI Operations Potential Exam Topics:

  • 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.

22%
Exam Weight

Recommended Training

Suggested Reading

Practice Questions

Explore sample questions to get familiar with the topics and question styles you may encounter on the certification exam.

Learning Mode

Answer each question and get immediate feedback, including the correct answer, an explanation, and a link to learn more.

Try Practice Questions in Learning Mode

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