NVIDIA AI Infrastructure and Operations (NCA-AIIO) Exam Questions 2025
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The NVIDIA-Certified Associate: AI Infrastructure and Operations (NCA-AIIO) certification validates foundational knowledge of AI infrastructure, accelerated computing, and operational best practices. It is designed for professionals working with AI and high-performance computing (HPC) environments.
Exam Overview
The NCA-AIIO exam tests a candidate’s understanding of AI infrastructure components, including GPUs, networking, storage, and cloud-based AI solutions. It is suitable for those looking to establish expertise in AI operations, infrastructure management, and deployment best practices.
Exam Details:
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Exam Duration: 1 hour
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Number of Questions: 50
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Exam Fee: $135
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Language: English
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Certification Level: Associate
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Validity: 2 years
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Passing Score: Typically around 70% (may vary)
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Format: Multiple-choice and scenario-based questions
Who Should Take This Exam?
This certification is ideal for individuals in technical and operational roles who manage AI workloads, infrastructure, and computing environments. Suitable candidates include:
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Data center technicians
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IT administrators and managers
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AI and cloud infrastructure engineers
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DevOps professionals
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Solution architects
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System and network administrators
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Sales engineers involved in AI solutions
It is recommended to have below knowledge when attempting NVIDIA AI Infrastructure and Operations (NCA-AIIO) Exam Questions 2025
The exam covers a broad range of topics essential for managing AI and accelerated computing environments:
1. Fundamentals of AI and Accelerated Computing
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Basics of AI, machine learning (ML), and deep learning (DL)
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Use cases and applications of AI in different industries
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Importance of accelerated computing for AI workloads
2. NVIDIA GPU Architecture
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Understanding GPU processing and parallel computing
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Differences between CPU and GPU for AI and ML workloads
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Features of modern NVIDIA GPUs (Tensor Cores, CUDA, etc.)
3. NVIDIA AI Software Suite
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Overview of key software tools like NVIDIA AI Enterprise, CUDA, RAPIDS, and TensorRT
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How NVIDIA software accelerates AI workloads
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Deep learning frameworks optimized for NVIDIA GPUs (PyTorch, TensorFlow, etc.)
4. Infrastructure and Operational Best Practices
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Managing AI workloads on-premises and in the cloud
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Best practices for data center infrastructure supporting AI
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Key considerations for networking, storage, and power requirements
5. Deployment and Optimization Strategies
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Deploying AI workloads on NVIDIA GPUs
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Performance tuning and optimization techniques
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Monitoring and maintaining AI infrastructure for efficiency
Preparation Strategy
Recommended Learning Path
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Self-Paced Online Courses – NVIDIA provides learning resources covering AI infrastructure and GPU computing.
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Hands-on Practice – Setting up and managing AI workloads using NVIDIA GPUs.
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Mock Exams and Practice Questions – Familiarize yourself with real exam-style questions.
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Official Documentation – Review NVIDIA’s software and hardware documentation.
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Community and Discussion Groups – Engage with other professionals to discuss best practices and exam strategies
Know more about the exam here : https://www.nvidia.com/en-us/learn/certification/ai-infrastructure-operations-associate/
Check our other Nvidia Certification offerings : https://skillcertpro.com/product-category/nvidia/
Lekan Adebayo –
The questions were well-suited, and the explanations provided at the end, along with the suggested reference materials, were extremely helpful. I aced the exam after reviewing these practice tests a few times. However, relying solely on these practice tests isn’t enough to pass the exam. Thorough preparation is essential, and these tests serve as a valuable supplement that greatly enhances your efforts.