Free NVIDIA NCA-AIIO Actual Exam Questions - Question 3 Discussion
complex mathematical calculations and real-time data analytics. The calculations are CPU-intensive,
requiring precise sequential processing, while the data analytics involves processing large datasets in
parallel. How should you allocate the workloads across GPU and CPU architectures?
C/D? GPUs can speed up some math if it’s parallelizable, but the question says it’s precise and sequential, so that’s tricky. Data analytics usually benefits more from GPU’s parallel power, so C feels cleaner here.
Not B, CPUs aren’t just for I/O; they’re best for sequential math here.
C. CPUs are generally better for precise, sequential tasks like complex math, while GPUs shine at handling large-scale parallel data, fitting the analytics workload perfectly. It just matches their strengths well.
Maybe C makes the most sense since CPUs handle sequential math better and GPUs are designed for parallel data crunching. The question highlights precise sequential processing, which fits CPUs more naturally.
Makes sense to go with C here. CPUs are definitely better for the CPU-heavy, sequential math stuff since they handle complex, step-by-step calculations well. Meanwhile, GPUs are built for crunching large data sets at the same time, so they fit perfectly with the parallel data analytics. A and B don't quite match the workloads properly, and D ignores the sequential nature of the math tasks. So C is the best split for this kind of job.
This one’s tricky but I’d ditch D right off the bat since GPUs aren’t great with precise sequential tasks needed for CPU-intensive calculations. So, C sounds better-CPUs handle the math because of their sequential processing strength, and GPUs deal with the parallel data analytics. That fits the workload nature better than B or A.