NVIDIA NCA-AIIO Exam Details & Actual Exam Questions
- Exam Code/Number: NCA-AIIO
- Exam Name/Title: NVIDIA-Certified Associate AI Infrastructure and Operations
- Certification Provider: NVIDIA
- Corresponding Certification: NVIDIA-Certified Associate
- Exam Questions: 125
- Updated On: Sep,15 2026
- Certification Level: Associate
NVIDIA-Certified Associate AI Infrastructure and Operations Exam Questions
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(45 Up Votes)NVIDIA NCA-AIIO Exam Overview:
| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA-Certified Associate AI Infrastructure and Operations |
| Exam Number: | NCA-AIIO |
| Exam Duration: | 60 minutes |
| Related Certifications: | NVIDIA-Certified Associate |
| Exam Format: | Multiple Choice, Simulation-Style Questions, Scenario-Based Items |
| Real Exam Qty: | 50 |
| Exam Price: | $125 USD |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Sample Questions: | NVIDIA NCA-AIIO Sample Questions |
| Exam Way: | Online, proctored remotely via Certiverse |
| Pre Condition: | A basic understanding of data center infrastructure |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/ai-infrastructure-operations-associate |
NVIDIA NCA-AIIO Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Essential AI Knowledge | 38% | - Explain the key AI use cases and industries - Differentiate the concepts of AI, machine learning, and deep learning - Describe the software components related to the life cycle of AI development and deployment - Compare and contrast training and inference architecture requirements and considerations - Compare and contrast GPU and CPU architectures - Describe the NVIDIA software stack used in an AI environment - Explain the purpose and use case of various NVIDIA solutions - Explain the factors contributing to recent rapid improvements and adoption of AI |
| AI Operations | 22% | - Identify the key considerations for virtualizing accelerated infrastructure - 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 |
| AI Infrastructure | 40% | - Explain the purpose and benefits of a DPU in a datacenter - Scale a GPU infrastructure for different use cases - Articulate the key advantages, challenges, and considerations related to on-prem vs cloud infrastructures - Identify high speed DC network options and their use cases - Identify facility requirements - Identify key components and considerations of a cluster of an accelerated infrastructure - Identify and describe DC networking protocols and key concepts - Identify hardware requirements for specific AI training task use cases - Identify key concepts, and high-level specifications related to power and cooling requirements within a datacenter - Determine networking requirements for AI workloads |
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