Free NVIDIA NCA-AIIO Actual Exam Questions - Question 13 Discussion

C makes more sense since RAPIDS focuses on data science workflows, not just hardware like DGX.

C imo since RAPIDS is built specifically for data analytics and ML acceleration on GPUs, making it ideal for scaling model development and deployment. DGX A100 is hardware, but the question focuses on the solution for analytics workloads.

It’s C, RAPIDS is the software stack made exactly for scaling analytics and ML on GPUs.

It’s D. While RAPIDS (C) is definitely the software side for speeding up analytics and ML on GPUs, the question mentions building and deploying models at scale, which points more to the full system solution. DGX A100 is designed exactly for that—high-performance hardware optimized for large-scale ML workloads. RAPIDS is great but it’s more about accelerating data science tasks on top of NVIDIA GPUs, not a standalone solution for scaling deployments like DGX. So D makes more sense if you consider the scale and deployment angle.

C/D? I’d rule out CUDA because it’s more like the programming framework rather than a full solution targeting analytics and ML specifically. JetPack is for edge devices, so not really relevant here. Between RAPIDS and DGX A100, RAPIDS is the specialized software stack that directly accelerates ML and data analytics workflows on GPUs, while DGX A100 is the high-performance hardware platform. The question’s emphasis on “build and deploy models at scale using GPUs” fits RAPIDS better since it provides those GPU-accelerated libraries data scientists use daily.

I’m thinking about CUDA (A) too since it’s the base programming platform for GPUs, but the question hints at a specialized solution for analytics and ML, which sounds more like RAPIDS (C). Could it be that CUDA is too general here?

Not B—it’s definitely more than just JetPack for embedded AI.

It’s C since RAPIDS is all about speeding up data processing and ML with GPU-accelerated libraries, unlike DGX A100 which is mainly hardware. RAPIDS fits the “build and deploy models” part better.

D imo, because DGX A100 is a full system optimized for big ML workloads, not just software.

C imo, RAPIDS is designed for data analytics and ML acceleration on GPUs.

It’s D because the DGX A100 is built for scalable ML and analytics workloads using GPUs.

Option C seems like the best fit here since RAPIDS is all about speeding up data analytics and ML on GPUs. CUDA (A) is more of a programming platform, not a ready-made solution for analytics. JetPack (B) is focused on AI at the edge, like robotics, and DGX A100 (D) is hardware, not software specifically designed for model building at scale. So, C stands out as the software toolkit geared exactly towards data scientists working with large-scale ML and analytics workloads.