NVIDIA NCA-AIIO Questions Answers
NVIDIA-Certified Associate AI Infrastructure and Operations- 71 Questions & Answers
- Update Date : October 19, 2026
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NVIDIA NCA-AIIO Sample Questions
Question # 1What is the maximum number of MIG instances that an H100 GPU provides?
A. 7
B. 8
C. 4
Question # 2
Which NVIDIA technology provides the broadest ecosystem for parallel computation across languages?
A. cuGraph
B. OpenCL
C. Triton Inference Server
D. CUDA
Question # 3
A simul-ation is bottlenecked by memory transfer speeds. Which GPU architectural feature addresses this?
A. Large shared memory and high-bandwidth buses.
B. Direct wiring of GPUs as main disk controllers.
C. Increase number of I/O ports for PCIe devices.
D. Dedicated and proprietary inference ASICs.
Question # 4
Which protocol is most critical for low-latency GPU-to-GPU transfers in large AI clusters using Ethernet?
A. DCTCP with ECN-based congestion control.
B. PFC-only lossless Ethernet without RDMA.
C. RDMA over Converged Ethernet, or RoCE.
D. iWARP, RDMA on TCP over Ethernet.
Question # 5
Which of the following is a best practice for addressing model drift in AI operations?
A. Increase hardware resources when accuracy drops.
B. Monitor deployed models regularly and retrain with fresh data.
C. Permit changes in input data distributions over time.
D. Allow the model to generalize to any data.
Question # 6
What distinguishes an edge AI deployment from cloud-based deployments?
A. Eliminates need for network management.
B. Processes data close to the source.
C. Relies solely on CPU for all computation.
D. Requires higher-capacity GPUs at every site.
Question # 7
Engineers are troubleshooting slow step time and poor scaling efficiency in a multi-rack distributed AI training cluster. Which infrastructure change is MOST likely to improve endto-end training performance?
A. Migrate inter-node communication to a secured Wi-Fi 6 mesh to reduce cabling complexity in the data center.
B. Deploy a lossless InfiniBand or RoCE-based high-bandwidth, low-latency fabric and tune it for all-reduce traffic.
C. Insert stateful firewalls with deep-packet inspection between training nodes to better control east-west traffic flows.
D. Increase the number of top-of-rack switch ports while keeping the same oversubscribed Layer 3 Ethernet design.
Question # 8
Which NVIDIA product is used for data preparation in an AI workflow?
A. DOCA
B. RAPIDS
C. DLSS
Question # 9
Which architecture, training or inference, requires more data storage?
A. Inference architecture requires more data storage.
B. Training architecture requires more data storage.
C. Training and inference architecture require the same amount of data storage.
Question # 10
In a large enterprise cluster, frequent out-of-memory errors occur mid-experiment. What operational feature resolves this?
A. Deploy and monitor containers to boost GPU memory.
B. Resource reservation and usage monitoring in the workload manager.
C. Increase cluster node count automatically.
Question # 11
NVIDIA AI Factories are designed primarily to support which part of the AI/MLOps pipeline?
A. Expansion of raw storage capacity without changing workflows.
B. Automated end-to-end handling of data, training, and deployment.
C. Long-term backup of unstructured data only.
D. Manual test environment setup for GPU driver comparisons.
Question # 12
What should an AI operations team do to maintain consistency when scaling workloads across different environments?
A. Boost hardware speed for every deployment.
B. Document differences between test and production.
C. Use containers to package dependencies for reproducibility.
Question # 13
Why use NVIDIA GPUDirect Storage in an AI cluster?
A. Improves TCP/IP data transmission speeds between GPUs and CPUs.
B. Simplifies traditional CPU-centric storage input/output paths.
C. Enables peer-to-peer memory transfers between GPUs and NVMe storage.
D. Increases GPU clock rate by offloading storage read/write.
Question # 14
What aspect of AI infrastructure design is MOST critical for ensuring high availability of production AI services during hardware or node failures?
A. Automated failover orchestration and elastic scaling across redundant nodes.
B. Custom GPU driver builds optimized for each application.
C. Periodic expansion of training datasets with backup copies.
D. Manual GPU restarts and ad hoc redeployment during incidents.
Question # 15
What is a direct benefit of using GPUDirect RDMA for multi-server workloads?
A. Raises GPU base memory clock speeds.
B. Offloads data movement from CPUs.
C. Allows CPUs to prioritize scheduling.
D. Compresses transferred data.