What is a primary limitation of using CPUs for large-scale AI model training?

They cannot execute any floating-point arithmetic

They consume more power per operation than any other chip

They are not compatible with common AI frameworks like TensorFlow

They have limited parallelism compared to specialized accelerators

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Domain 4Objective 2

Describe Compute Architectures Used with AI Workloads (CPU, GPU, TPU, FPGA) CERTIFIED-AI-EXPERT Practice Questions (Page 5)

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 21 practice questions to prepare you well beyond it. (estimate)

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Questions 21–21

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What is a primary limitation of using CPUs for large-scale AI model training?
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Describe Compute Architectures Used with AI Workloads (CPU, GPU, TPU, FPGA) CERTIFIED-AI-EXPERT Practice Questions (Page 5)

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What is a primary limitation of using CPUs for large-scale AI model training?

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