Note: NVIDIA is a trademark of NVIDIA Inc. This course material is not affiliated with or endorsed by NVIDIA.

Additional Material

1. Study Material [PDF]: Before you begin, download the 160-page PDF file (Newly Released) provided under the Resources link in the first question of the first practice paper. This latest, updated document contains essential reference material you will need throughout the course to maximize your learning and ensure you are preparing with the most current certification content.

2. Interview Questions & Answers [PDF]: Please download additional 200+ Interview questions and answers for the Snowflake Core. You must go through all these interview questions before your real certification exam as well as before your interview process. To download the same please go through Question-1 resources section in Practice Paper-1.

3. Interview Questions & Answers (Audio Book – 2 Hr 30 Mins): Listen and learn on the go with the audiobook version of interview questions and answers. The download link is provided in the Resources section of Question 1.

Disclaimer: “This course is not affiliated with or endorsed by NVIDIA. NVIDIA and CUDA are trademarks of NVIDIA Corporation.”

Prepare with confidence for the Certified Associate AI Infrastructure and Operations (NCA-AIIO) certification with this comprehensive, exam-focused practice test and preparation course. This entry-level certification validates your foundational skills in AI infrastructure, accelerated computing, and operational best practices—critical knowledge for IT professionals working in the rapidly evolving field of artificial intelligence.

The NCA-AIIO exam consists of 200+ multiple-choice questions to be completed in 180 (each paper) minutes, and it covers a wide range of essential topics, including:

• AI, machine learning, and deep learning fundamentals

• Accelerated computing use cases in enterprise environments

• GPU and DPU architecture vs. traditional CPUs

• hardware and software ecosystem

• Networking and storage requirements for AI workloads

• Data center operations optimized for AI

• Cluster orchestration, job scheduling, and monitoring

• Virtualization and containerization for AI deployments

• Performance optimization and operational troubleshooting

This course is designed for data center engineers, AI/ML infrastructure specialists, cloud and solutions architects, DevOps engineers, system administrators, and technical pre-sales professionals—as well as any IT professional preparing for the NCA-AIIO certification exam.

What makes this course unique:

• Realistic practice tests that replicate the actual exam format and difficulty level

• Detailed explanations for every question to strengthen conceptual understanding

• Exam strategies and tips to help you manage time and avoid common mistakes

• Content designed to reinforce real-world, job-ready AI infrastructure skills

By the end of this course, you will be able to:

• Explain the fundamentals of AI workloads and their use cases

• Differentiate between AI, ML, and deep learning in practical terms

• Describe AI ecosystem, including GPUs, DPUs, and related software

• Apply best practices for AI data center operations, networking, and storage

• Prepare effectively and confidently for the NCA-AIIO certification exam

Whether you are upskilling for career growth, transitioning into AI infrastructure, or targeting this certification to validate your expertise, this course will equip you with the knowledge and confidence to succeed.

Enroll now and take the first step toward becoming a certified AI Infrastructure and Operations professional, ready to contribute to high-performance AI deployments in enterprise environments.

Who this course is for:

• Data center operations engineers who need to support AI workloads and understand AI-specific infrastructure requirements.
• Technical pre-sales and solution specialists who must explain AI architecture, software stack, and deployment considerations to customers.
• AI/ML infrastructure engineers transitioning from model development to production environments, needing to grasp the operational stack.
• System administrators and platform engineers tasked with cluster orchestration, job scheduling, monitoring, and virtualization for AI systems.
• Network and compute architects designing environments optimized for GPU/DPU-accelerated AI performance.
• DevOps and site reliability engineers integrating AI services into enterprise pipelines and ensuring reliable, scalable operations.
• Cloud and solutions architects building or advising on hybrid/on-prem AI deployments with echnologies.
• Aspiring certification candidates preparing for the Certified Associate AI Infrastructure and Operations (NCA-AIIO) exam.
• Technical leads or managers who need to assess readiness, define best practices, or onboard teams for AI infrastructure projects.