Free NVIDIA NCA-AIIO Actual Exam Questions - Question 10 Discussion
requires both data preprocessing and training across multiple GPUs. They need to ensure that the
GPUs are used efficiently to minimize training time. Which combination of NVIDIA technologies
should they use?
B tbh, DALI and NCCL in option C do sound solid for preprocessing and multi-GPU syncing, but just to add—DeepStream SDK (part of B) is more for video analytics, so that’s probably not relevant here. CUDA Toolkit, however, is the base for GPU programming, so it’s essential. Since the question emphasizes data preprocessing plus multi-GPU training efficiency, the combo in C really fits best. The others either focus on inference optimization or specific OS/catalog stuff, which doesn’t directly tackle the data loading and communication challenge this NLP team faces.
It’s C because DALI is great for speeding up data loading and preprocessing, while NCCL is built specifically for efficient GPU communication, which is crucial when training across multiple GPUs. Other options don’t handle both parts as well.
It’s definitely C. DALI handles the heavy lifting on the data preprocessing side, making sure that part doesn’t bottleneck the GPUs, while NCCL optimizes data exchange during training across multiple GPUs. The other options either focus on inference optimization or are more general-purpose without specifically addressing multi-GPU efficiency and data prep together. So for speeding up both preprocessing and training on several GPUs, C nails it.
Option C also fits because NCCL is designed for multi-GPU communication, which is key here.
C imo, since DALI speeds up data preprocessing on the GPU and NCCL handles communication between GPUs, making them the best combo for multi-GPU NLP training. The others don’t cover both parts as well.
B tbh, DeepStream is mostly for video analytics, so it’s probably not the best fit here. CUDA Toolkit is essential but alone doesn’t cover efficient multi-GPU data preprocessing and training like DALI and NCCL do.
C