AWS Certified Solutions Architect Associate SAA-C03 Practice Question
A multinational organization is deploying numerous environmental sensors across various locations to monitor and analyze ecological data in real-time. The data volume is substantial, and continuously streaming it back to a central processing location is becoming exceedingly costly because of the associated bandwidth usage. Which service should be used to preprocess and minimize the datasets locally before sending the refined data to the central system, thereby reducing transmission costs?
Leverage local data centers to bring cloud services closer to metropolitan areas
Employ portable edge computing and storage devices for large-scale data transfers
Utilize mobile edge computing infrastructure designed for telecom networks
Implement an IoT edge computing platform for local data processing
The option involving Wavelength is aimed primarily at applications that necessitate ultra-low latency for mobile devices and is not particularly suited for IoT sensor data. The concept of Local Zones is more about improving end user experience by reducing latency for interactive applications and is not primarily intended for local data processing scenarios. Snowball Edge is typically used for edge storage and batch data transfer rather than continuous, real-time edge processing. The correct choice, IoT Greengrass, is designed to allow connected devices to run local compute, message, data caching, and synchronization tasks. This includes executing functions, processing data streams, and only transmitting essential or processed data to the cloud, which aligns perfectly with the need to preprocess and reduce datasets at the source to save on transmission costs.
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