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NCP-ADS
Data Manipulation and Software Literacy
hard
Question 2 of 38

In an MLOps benchmarking session, the team must make the most defensible recommendation. Which recommendation best addresses unchanged pandas scenario?

AChoose cudf.pandas when the team wants GPU acceleration for an existing pandas workflow without rewriting that workflow.
BChoose cuML because machine learning estimators are the primary tool for pandas-compatible joins and groupbys.
CChoose a single fixed tool for every dataset size instead of matching the library to the scaling need.
DUse a generic CPU-only workflow without evaluating the documented GPU-accelerated option.

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