Data Preprocessing & CuDF — NCA-GENL Practice Question
A representative NVIDIA Generative AI LLMs Associate (NCA-GENL) exam question on Data Preprocessing & CuDF. Work through it below, then read why each option is right or wrong.
Short answer
The correct answer is C. Replace pandas imports with cuDF from the RAPIDS library to leverage GPU-accelerated DataFrames.
cuDF provides a pandas-like API that runs on GPU, meaning most existing pandas code can be migrated with minimal changes (often just swapping the import statement). This leverages the DGX system's A100 GPUs for massive speedups on DataFrame operations. Option B would require a complete rewrite and different infrastructure. Option C does not actually speed up the total computation time. Option D changes the format but does not address the fundamental CPU bottleneck.
The Question
A data engineer is preparing a 200 GB text corpus for LLM fine-tuning and notices that pandas-based preprocessing is taking over 18 hours on a CPU server. The team has access to a DGX system with multiple A100 GPUs. Which approach would most effectively accelerate the preprocessing pipeline while requiring minimal code changes?
Why C is correct
cuDF provides a pandas-like API that runs on GPU, meaning most existing pandas code can be migrated with minimal changes (often just swapping the import statement). This leverages the DGX system's A100 GPUs for massive speedups on DataFrame operations. Option B would require a complete rewrite and different infrastructure. Option C does not actually speed up the total computation time. Option D changes the format but does not address the fundamental CPU bottleneck.
Why the other options are wrong
Option A does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (C) is the only one that fully meets every constraint stated in the question.
Option B does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (C) is the only one that fully meets every constraint stated in the question.
Option D does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (C) is the only one that fully meets every constraint stated in the question.
Key idea: Data Preprocessing & CuDF
cuDF provides a pandas-like API that runs on GPU, meaning most existing pandas code can be migrated with minimal changes (often just swapping the import statement). This leverages the DGX system's A100 GPUs for massive speedups on DataFrame operations. Option B would require a complete rewrite and different infrastructure. Option C does not actually speed up the total computation time. Option D changes the format but does not address the fundamental CPU bottleneck. On the NCA-GENL exam, questions in the "Data Analysis and Visualization" domain test whether you can map a scenario's constraints to the right choice. Read the requirement carefully, eliminate options that violate any single constraint, and pick the one that satisfies all of them with the least operational overhead.
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