NVIDIA · NCA-AIIO
Validates foundational concepts of adopting AI computing related to infrastructure and operations, including GPU processing, parallel computing, and AI workload management.
Questions
715
Duration
60 minutes
Passing Score
Pass/Fail
Difficulty
AssociateLast Updated
Jan 2026
Use this NCA-AIIO practice exam to prepare for NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 715 questions for NVIDIA NCA-AIIO, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to patterns in your missed answers. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
The NVIDIA-Certified Associate: AI Infrastructure and Operations (NCA-AIIO) is an entry-level credential that validates foundational knowledge of adopting AI computing within enterprise and data center environments. The certification covers three core domains: essential AI knowledge (including GPU vs. CPU architectures, ML/DL concepts, and the NVIDIA software stack), AI infrastructure (hardware requirements, GPU scaling, power and cooling, networking protocols, and on-premises vs. cloud considerations), and AI operations (cluster orchestration, GPU monitoring, and virtualization). It is designed to confirm that candidates understand how AI workloads are deployed, managed, and optimized on modern accelerated computing platforms.
The exam is proctored online through the Certiverse platform, costs $125, and is valid for two years from the date of issuance. It reflects NVIDIA's push to establish a standardized baseline of AI infrastructure literacy across IT and operations roles, complementing more advanced credentials such as the NVIDIA-Certified Professional: AI Infrastructure (NCP-AII).
This certification is intended for early-career and mid-level professionals who work with or adjacent to AI computing infrastructure. Relevant roles include data center technicians, IT managers, system and network administrators, DevOps and MLOps engineers, solution architects, and sales or pre-sales engineers who need to articulate AI infrastructure concepts to technical customers.
It is also well-suited for students in computer science, data science, networking, or information systems who want a vendor-recognized credential to validate their foundational understanding. No prior AI research or data science background is required — the target candidate is someone on the infrastructure and operations side who needs to support or design environments where AI workloads run.
NVIDIA recommends a basic understanding of data center infrastructure as the primary prerequisite. Familiarity with concepts such as servers, networking hardware, and storage systems will provide meaningful context for the AI-specific content covered on the exam. There are no formal certification prerequisites or mandatory training requirements.
Candidates benefit from hands-on exposure to enterprise IT environments, particularly experience with GPU servers, virtualization platforms, or cloud infrastructure. Completing the NVIDIA Academy self-paced course 'AI Infrastructure and Operations Fundamentals' (approximately 7 hours) is the most direct preparation path and aligns closely with the exam's three domains.
The NCA-AIIO exam consists of 50 scored questions delivered in 60 minutes via online remote proctoring through the Certiverse platform. The exam is administered in English. Candidates must create a Certiverse account to register and schedule the exam. The exam fee is $125, and it can also be purchased bundled with the official NVIDIA Academy preparation course for $150.
The scoring system is reported as Pass/Fail. NVIDIA has not published a specific numeric cut score. The certification remains valid for two years, after which recertification requires retaking the current version of the exam. Upon passing, candidates receive a digital badge and an optional printed or digital certificate.
Earning the NCA-AIIO credential signals validated, vendor-neutral-adjacent knowledge of AI infrastructure to employers who are actively deploying or expanding GPU-based computing environments. It is particularly relevant for IT and operations professionals looking to transition into AI infrastructure roles or demonstrate credibility in conversations with data science and ML engineering teams. The certification pairs well with roles such as AI Infrastructure Engineer, Data Center Operations Specialist, MLOps Engineer, and Solutions Architect focused on accelerated computing.
As enterprise AI adoption continues to expand GPU deployments across both on-premises data centers and cloud environments, professionals with documented AI infrastructure fluency are in growing demand. The NCA-AIIO serves as a stepping stone toward the more advanced NVIDIA-Certified Professional: AI Infrastructure (NCP-AII) credential, creating a defined certification progression path within the NVIDIA ecosystem. The digital badge issued upon passing can be shared on LinkedIn and professional portfolios to increase visibility with recruiters hiring for AI-adjacent infrastructure roles.
5 sample questions with answers and explanations. Start a practice session to test yourself across all 715 questions.
Preview — answers shown1. Woodgrove Bank is upgrading their AI infrastructure and comparing memory specifications between GPU generations. What is the total HBM3e memory capacity available on the NVIDIA H200 Tensor Core GPU?
Explanation
The NVIDIA H200 Tensor Core GPU features 141 GB of HBM3e memory, making it the first GPU to offer this capacity. This represents a 76% increase over the H100's 80 GB of HBM3 memory. The 120 GB and 192 GB figures are not associated with current NVIDIA GPU offerings.
2. Litware is configuring GPU memory utilization in vLLM. What parameter controls the percentage of GPU memory pre-allocated for the KV cache?
Explanation
The gpu-memory-utilization parameter in vLLM controls what percentage of the GPU's memory should be pre-allocated for the KV cache and other inference operations. This allows tuning the trade-off between memory usage and batch capacity. max-memory, kv-cache-size, and memory-fraction are not the actual vLLM parameter names.
3. Fabrikam needs to specify the port NCCL uses for socket communication. Which environment variable sets the NCCL socket port?
Explanation
NCCL_COMM_ID can specify a particular address:port for the bootstrap communication. However, NCCL typically handles port selection automatically. For socket-based communication, NCCL_SOCKET_IFNAME specifies the interface, and the port is managed by the bootstrap mechanism using NCCL_COMM_ID format.
4. Wide World Importers wants to verify MIG is working correctly. Which nvidia-smi output confirms a MIG compute instance is properly created?
Explanation
nvidia-smi mig -lci lists compute instances, confirming they are properly created within GPU instances. A compute instance is required for CUDA workloads to run on a MIG partition. GPU instances (-lgi) alone are not sufficient - compute instances must be created within GPU instances for workloads.
5. Fabrikam needs to understand memory utilization breakdown. What is the difference between memory used and memory reserved in nvidia-smi?
Explanation
Memory used shows actively allocated memory by applications. Reserved memory includes driver overhead, CUDA context, and memory reserved for potential use. The difference explains why available memory is less than total minus used. Understanding this helps with memory planning.
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