Which NVIDIA solution is specifically designed to accelerate data analytics and machine learning workloads, allowing data scientists to build and…

NVIDIA RAPIDS is an open-source suite of GPU-accelerated libraries specifically designed to speed up

data analytics and machine learning workflows. It enables data scientists to leverage GPU parallelism

to process large datasets and build machine learning models at scale, significantly reducing

computation time compared to traditional CPU-based approaches. RAPIDS includes libraries like

cuDF (for dataframes), cuML (for machine learning), and cuGraph (for graph analytics), which

integrate seamlessly with popular frameworks like pandas, scikit-learn, and Apache Spark.

In contrast:

NVIDIA CUDA(A) is a parallel computing platform and programming model that enables GPU

acceleration but is not a specific solution for data analytics or machine learning—it’s a foundational

technology used by tools like RAPIDS.

NVIDIA JetPack(B) is a software development kit for edge AI applications, primarily targeting NVIDIA

Jetson devices for robotics and IoT, not large-scale data analytics.

NVIDIA DGX A100(D) is a hardware platform (a powerful AI system with multiple GPUs) optimized for

training and inference, but it’s not a software solution for data analytics workflows—it’s the

infrastructure that could run RAPIDS.

Thus, RAPIDS (C) is the correct answer as it directly addresses the question’s focus on accelerating

data analytics and machine learning workloads using GPUs.

Reference:NVIDIA RAPIDS documentation on nvidia.com; NVIDIA AI Infrastructure overview.