Exam NCP-AII Topic 1 Question 11 Discussion

Actual exam question for NVIDIA's NCP-AII exam
Question #: 11
Topic #: 1

You are expanding a DGX-based deep learning cluster to train on large, high-resolution images that cannot fit into local cache. Multiple nodes will access this data concurrently and require high performance. Which storage and networking solution best meets these requirements?

A. Increase the SSD RAID-0 local cache size in each node so it can absorb most training data, making network storage type and speed less important for performance. B. Implement a standard NFS server on a 10GbE network because the cluster can access the export and job performance will not be impacted. C. Deploy a high-performance parallel file system across InfiniBand or 40/100GbE, ensuring at least 3 GB
/s per node and scalable aggregate bandwidth for all cluster workloads. D. Recommend general-purpose object storage for all training data because it is optimized for deep learning workloads and distributed data access at any scale.

Suggested Answer: C Vote an answer

The correct solution is a high-performance parallel file system connected through InfiniBand or high-speed Ethernet. Large image training workloads can generate heavy concurrent reads from many GPU nodes, especially when datasets cannot be cached locally. If storage cannot deliver enough per-node and aggregate throughput, GPUs wait for input data and overall training efficiency drops. A parallel file system is designed to distribute metadata and data access across multiple storage servers, allowing many DGX nodes to read training data concurrently. Using InfiniBand or 40/100GbE provides the network bandwidth needed to avoid storage becoming the limiting factor. Expanding local RAID-0 cache may help some repeated reads, but it does not solve shared dataset access or scale well across many nodes. A single NFS server on 10GbE is likely to bottleneck under concurrent multi-node training. General object storage is useful for capacity and cloud- style workflows, but it is not always optimal for high-throughput POSIX-style training access. For DGX cluster optimization, storage must be sized for sustained throughput, metadata operations, and concurrent GPU consumption.

by Sara at Jun 04, 2026, 08:37 PM

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