NCA-AIIO learning guides

One short guide per official objective, grouped by exam domain. Each point is backed by a quote from an NVIDIA source. Terms link to the glossary the first time they appear.

Essential AI Knowledge

38% of the exam · 8 objectives

1.1 The NVIDIA software stack
1.2 Training vs inference
1.3 AI, machine learning and deep learning
1.4 Why AI took off
1.5 AI use cases and industries
1.6 NVIDIA solutions and what they are for
1.7 The AI development and deployment life cycle
1.8 GPU vs CPU architecture

AI Infrastructure

40% of the exam · 10 objectives

2.1 Hardware for training workloads
2.2 Scaling GPU infrastructure
2.3 Power and cooling
2.4 On-prem vs cloud
2.5 Components of an accelerated cluster
2.6 Facility requirements
2.7 Networking requirements for AI
2.8 Data center networking protocols
2.9 High-speed network options
2.10 DPUs in the data center

AI Operations

22% of the exam · 4 objectives

3.1 Data center management and monitoring
3.2 Orchestration and job scheduling
3.3 Monitoring GPUs
3.4 Virtualizing GPUs