試験NCA-AIIO トピック1 問題51 スレッド

Real-time monitoring of high-volume social media data requires rapid data ingestion, processing, and visualization, which NVIDIA GPUs can accelerate. A GPU-accelerated time-series database (e.g., tools like NVIDIA RAPIDS integrated with time-series frameworks or custom CUDA implementations) leverages GPU parallelism for fast data ingestion and preprocessing, while also enabling real-time visualization directly on the GPU. This approach minimizes latency and maximizes throughput, aligning with NVIDIA's emphasis on end-to-end GPU acceleration in DGX systems and data analytics workflows.
A relational database (Option A) lacks GPU acceleration and struggles with real-time scalability. Using a GPU model with CPU visualization (Option B) introduces a bottleneck, as CPUs can't keep up with GPU- processed data rates. CPU-based ETL (Option C) is too slow for real-time needs compared to GPU alternatives. Option D fully utilizes NVIDIA GPU capabilities, making it the most effective choice.