NVIDIA-Certified Professional Accelerated Data Science

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Q_01 domain/d1 · medium single discuss →

A data science team runs analysis workflows on large tabular datasets using pandas, but iteration speed is limited by CPU processing. They want to accelerate their existing pandas code on GPUs without rewriting their scripts. Which CUDA-X library is designed to meet this need?

A cuML, with drop-in accelerators for scikit-learn
B cuGraph, with a zero-code-change accelerator for NetworkX
C cuDF, with drop-in accelerators for popular DataFrame libraries like pandas
D cuxfilter, for interactive multidimensional filtering

Q_02 domain/d1 · medium single discuss →

A retail organization needs to make operational decisions using the latest transaction data, but their current analytics jobs take days to complete, delaying insights. According to NVIDIA's stated use cases, what is the primary benefit of applying GPU acceleration to this workload?

A It automatically converts SQL queries into machine learning models
B It lets businesses work with up-to-date information and get insights faster, reducing processing time from days to hours
C It eliminates the need for a data warehouse entirely
D It guarantees higher prediction accuracy for all models