Passing exam with NCP-AII latest training materials
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Updated: Nov 02, 2026
No. of Questions: 197 Questions & Answers with Testing Engine
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NVIDIA NCP-AII Exam Syllabus Topics:
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Reference: https://www.nvidia.com/en-us/learn/certification/ai-infrastructure-professional/
NVIDIA NCP-AII Exam Overview:
| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA AI Infrastructure (NCP-AII) Certification Exam |
| Exam Number: | NCP-AII |
| Exam Price: | $400 |
| Related Certifications: | NVIDIA-Certified Professional AI Networking (NCP-AIN) NVIDIA-Certified Professional AI Operations (NCP-AIO) NVIDIA-Certified Associate AI Infrastructure and Operations (NCA-AIIO) |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 70-75 |
| Exam Format: | Multiple-choice, Scenario-based questions |
| Available Languages: | English |
| Recommended Training: | AI Infrastructure Professional Workshop AI Infrastructure & Operations Fundamentals (NVIDIA Training) |
| Exam Registration: | Official NVIDIA Certification Portal NVIDIA AI Infrastructure Certification Page |
| Sample Questions: | NVIDIA NCP-AII Sample Questions |
| Exam Way: | Online proctored exam (remote) or authorized test center depending on region |
| Pre Condition: | Recommended 2–3 years of experience working in data center environments with NVIDIA hardware solutions (GPU servers, networking, storage). |
| Official Syllabus URL: | https://www.nvidia.com/en-eu/learn/certification/ai-infrastructure-professional/ |
NVIDIA AI Infrastructure Sample Questions:
An AI training cluster with NVIDIA GPUs experiences prolonged data loading times during checkpoint reloading, causing GPUs to idle frequently, CPU utilization during data transfers remains high. Which solution most effectively optimizes storage-to-GPU throughput while reducing CPU overhead?
- A. Implement GPUDirect Storage to enable direct data transfers.
- B. Migrate datasets to SATA SSDs with RAID 0 for higher sequential read speeds.
- C. Add more GPUs to the cluster to parallelize data loading tasks.
- D. Increase batch sizes to reduce the frequency of storage access.
Correct Answer: A 🗳️
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An InfiniBand administrator needs to run performance benchmarks on new devices added to the fabric. What tool should be used to check the latency?
- A. perfmon
- B. tcpdump
- C. ib_write_lat
- D. ibdiagnet
Correct Answer: C 🗳️
Explanation: Only visible for TrainingQuiz members. You can sign-up / login (it's free).
A data scientist wants to run a PyTorch container on docker using a Multi-Instance GPU (MIG) slice. Based on the requirements, the system administrate suggests using a MIG slice with 2g compute profile and 10GB of GPU memory. What command should the administrator tell the data scientist to run?
- A. docker run --mig '"device=0:2"' nvcr.io/nvidia/pytorch:20.11-py3 nvidia-smi -L
- B. docker run --gpus '"device0"' --mig "2" nvcr.io/nvidia/pytorch:20.11-py3 nvidia-smi -L
- C. docker run --mig '"device=0:2g.10gb" nvcr.io/nvidia/pytorch:20.11-py nvidia-smi -L
- D. docker run --gpus '"device=0:2"' nvcr.io/nvidia/pytorch:20.11-py3 nvidia-smi -L
Correct Answer: D 🗳️
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A data scientist needs to run a Jupyter notebook on specific GPU cards. What command should be used?
- A. docker run --runtime=nvidia -dpu "1,2" -p 8888:8888 jupyter/scipy-notebook:2023-09-25
- B. docker run --gpus all --limit-gpu ""device=UUID-ABCDEF" -p 8888:8888 jupyter/scipy- notebook:2023-09-25
- C. docker run --dpu "1,2" -p 8888:8888 jupyter/scipy-notebook:2023-09-25
- D. docker run --gpus I"device=UUID-ABCDEF,1" -p 8888:8888 jupyter/scipy-notebook:2023-09-25
Correct Answer: D 🗳️
Explanation: Only visible for TrainingQuiz members. You can sign-up / login (it's free).
You are training a deep neural network using NCCL to coordinate communication across four GPUs in a single node. During early performance testing, you notice inconsistent scaling and longer-than-expected training times, even though all GPUs are being used. Which strategy would most effectively improve NCCL efficiency and collective operation performance in this setting?
- A. Assign the largest possible workload to the first GPU to maximize its utilization, and allow the remaining GPUs to process smaller or variable batch sizes as needed.
- B. Disable automatic load balancing so that the deep learning framework can dynamically assign samples to any GPU available during each iteration.
- C. Increase the communication frequency between GPUS allowing workloads to be unevenly split, so that synchronization is more frequent and model updates happen faster.
- D. Adjust the batch size so that each GPU receives an equalized portion of the batch, ensuring all GPUs process similar workloads end communication is evenly distributed.
Correct Answer: D 🗳️
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