NVIDIA Associate AI Infrastructure Operations NCA-AIIO Tests

Prepare for and pass the NCA-AIIO certification from NVIDIA. The NVIDIA Associate AI Infrastructure and Operations certification, otherwise known as the NCA-AIIO certification is an entry-level credential that validates the foundational concepts of AI computing related to infrastructure and operations. This exam is designed for IT professionals new to AI operations and infrastructure, who are required to understand and describe the various components and aspects of adopting AI in data center environments and on-premises environments. This certification is appropriate for a wide variety of job roles, from technical pre-sales to data center operations.

This exam covers the following three knowledge areas and learning objectives:

Essential AI knowledge: Exam Weight 38%

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.

1.1 Describe the NVIDIA software stack used in an AI environment.

1.2 Compare and contrast training and inference architecture requirements and considerations.

1.3 Differentiate the concepts of AI, machine learning, and deep learning.

1.4 Explain the factors contributing to recent rapid improvements and adoption of AI.

1.5 Explain the key AI use cases and industries.

1.6 Explain the purpose and use case of various NVIDIA solutions.

1.7 Describe the software components related to the life cycle of AI development and deployment.

1.8 Compare and contrast GPU and CPU architectures

AI Infrastructure: Exam Weight 40%

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

2.1 Identify hardware requirements for specific AI training task use cases.

2.2 Scale a GPU infrastructure for different use cases.

2.3 Identify key concepts and high-level specifications related to power and cooling requirements within a data center.

2.4 Articulate the key advantages, challenges, and considerations related to on-prem vs cloud infrastructures.

2.5 Identify key components and considerations of a cluster of an accelerated infrastructure.

2.6 Identify facility requirements.

2.7 Determine networking requirements for AI workloads.

2.8 Identify and describe data center networking protocols and key concepts.

2.9 Identify high-speed data center network options and their use cases.

2.10 Explain the purpose and benefits of a DPU in a data center

AI Operations: Exam Weight 22%

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.

3.1 Describe AI data center management and monitoring essentials.

3.2 Describe AI cluster orchestration and job scheduling essentials.

3.3 Articulate the key measures and criteria related to monitoring GPUs.

3.4 Identify the key considerations for virtualizing accelerated infrastructure