What are two use cases for using a graphic processing unit (GPU)? (Select 2)

Question

Options

• AProgrammable for a particular application-specific purpose.
• BImprove performance by accelerating networking hardware.
• CModel and analyze signal data streams in real time.
• DIsolate tenants from host management in a cloud landlord-tenant setting.
• EAccelerate HPC and Al by using financial data for analysis of risk and return.

• B
  4% (2)
• C
  93% (42)
• D
  2% (1)

Explanation

GPUs excel at massively parallel computation, making them ideal for tasks requiring simultaneous processing of large datasets. Option C is correct because real-time signal data analysis (e.g., radar, audio, or sensor streams) demands parallel processing of thousands of data points at once - a GPU's core strength. Option E is correct because HPC and AI workloads in finance (e.g., Monte Carlo simulations for risk modeling) are computationally intensive and parallelizable, exactly where GPUs outperform CPUs.

Why the distractors are wrong:

• A describes an ASIC (Application-Specific Integrated Circuit) or FPGA - hardware hardwired or programmed for one purpose, unlike the general-purpose parallel architecture of a GPU.
• B describes SmartNICs or network accelerator cards, which offload networking tasks - unrelated to GPU function.
• D describes hypervisor/virtualization technology, which enforces tenant isolation in cloud environments - a software/hardware security boundary, not a GPU use case.

Memory tip: Think "GPU = parallel horsepower." Whenever a question involves crunching many numbers simultaneously - signals, simulations, AI training, financial modeling - GPU fits. If the answer sounds like networking gear, security isolation, or custom-built chips, it's pointing elsewhere.

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