NVIDIA NCA-AIIO Exam Syllabus
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Before starting your NCA-AIIO exam preparation, it is recommended to review the complete NVIDIA AI Infrastructure and Operations exam syllabus and carefully go through the exam objectives listed below. Once you understand the exam structure and objectives, you should practice using our free NCA-AIIO questions. We also provide premium NCA-AIIO practice test, fully updated according to the latest exam objectives, to help you accurately assess your preparedness for the actual exam.
NVIDIA NCA-AIIO Exam Objectives
| Section | Weight | Objectives |
|---|---|---|
| Essential AI knowledge | 38% | 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 the 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 1.8 Compare and contrast GPU and CPU architectures 1.8 Compare and contrast GPU and CPU architectures 1.8 Compare and contrast GPU and CPU architectures |
| AI Infrastructure | 40% | Inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, and drawing conclusions, and supporting decision-making. 2.1 Understand the process of extracting insights from large datasets using data mining, data visualization, and similar techniques. 2.2 Compare models using statistical performance metrics, such as loss functions or proportion of explained variance. 2.3 Conduct data analysis under the supervision of a senior team member. 2.4 Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software 2.5 Identify relationships and trends or any factors that could affect the results of research |
| AI Operations | 22% | 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 |
| Official Information | https://www.nvidia.com/en-us/learn/certification/ai-infrastructure-operations-associate/ |
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