
NetAppCertified AI Expert
Domain 4Objective 3
Describe Network Architecture Used with AI Workloads (Ethernet vs Infiniband, RDMA, GPUDirect Storage) CERTIFIED-AI-EXPERT Practice Questions (Page 1)
Part of the AI Hardware Architectures domain, which accounts for 18% of the CERTIFIED-AI-EXPERT exam. NetApp does not publish an official question count, but from its 90-minute exam (~35–60 total, ~6–11 in this domain), expect 1–2 from this objective — we provide 20 practice questions to prepare you well beyond it. (estimate)
20questions here
4free pages
3concepts
18%of the exam
Questions 1–5
- 1
A data scientist is running a training job that reads large datasets from a parallel file system. The job is bottlenecked by the time it takes to move data from storage to GPU memory. They are considering using GPUDirect Storage (GDS) to improve performance. Which network architecture is required to fully benefit from GDS?
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What is the main purpose of GPUDirect Storage (GDS) in AI workloads?
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A data scientist is running a training job that reads a large dataset from a network-attached storage system. They notice that the GPU utilization is low, and the CPU is heavily utilized. They suspect the data loading path is the bottleneck. Which technology would most directly help them improve GPU utilization?
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An AI company is planning to deploy GPUDirect Storage (GDS) to speed up data loading. Their storage system is connected via a standard TCP/IP network. They are considering upgrading to either InfiniBand or RoCEv2 to enable GDS. The storage system is older and only supports RoCEv1. What is the best course of action?
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An AI platform team is evaluating ways to reduce CPU overhead on their GPU servers. They notice that during distributed training, the CPU is heavily loaded with copying data between kernel buffers and application memory for network communication. Which technology is specifically designed to address this overhead?
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