Describe Compute Architectures Used with AI Workloads (CPU, GPU, TPU, FPGA) CERTIFIED-AI-EXPERT Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   How do GPUs accelerate AI workloads?

   • ABy using thousands of cores to perform many calculations simultaneously
   • BBy executing instructions sequentially at very high clock speeds
   • CBy using a single powerful core with a large cache
   • DBy reconfiguring their hardware logic to match the neural network
   Reveal Solution Discussion Select an answer first
2. 7foundation · easy

   Which compute architecture offers the highest flexibility for a wide range of AI tasks, including both training and inference?

   • ACPU
   • BTPU
   • CGPU
   • DFPGA
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4. 8foundation · easy

   What is the primary design purpose of a Tensor Processing Unit (TPU)?

   • AReconfigurable hardware logic for custom AI algorithms
   • BLow-latency sequential processing for real-time control systems
   • CHigh-throughput tensor operations for neural network inference and training
   • DGeneral-purpose computing for a wide range of applications
   Reveal Solution Discussion Select an answer first
5. 9application · medium

   A company runs a batch inference pipeline for a natural language processing model. The workload is highly parallel, with thousands of independent inference requests that must complete within a tight latency window. The team has access to a pool of high-core-count CPUs and a pool of GPUs. Which architecture should they choose to meet the latency requirement while maximizing throughput?

   • AUse the CPU pool because GPUs are only effective for training, not for inference.
   • BUse the GPU pool because GPUs can process many inference requests concurrently through their thousands of cores.
   • CUse the CPU pool because CPUs have higher clock speeds and lower per-core latency.
   • DUse a mix of both, but assign the latency-critical requests to CPUs and the rest to GPUs.
   Reveal Solution Discussion Select an answer first
6. 10foundation · easy

   In an AI workload pipeline, which of the following is a typical role for a CPU?

   • AReconfiguring hardware logic to match the model architecture at runtime
   • BExecuting thousands of threads simultaneously for neural network inference
   • CPerforming the bulk of matrix multiplications during model training
   • DOrchestrating data preprocessing and coordinating accelerator operations
   Reveal Solution Discussion Select an answer first