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Answer: Design a distributed processing architecture that can scale out based on the incoming data volume and implement a machine learning model to detect anomalies in real-time.
To detect and respond to anomalies in energy usage patterns in real-time, a distributed processing architecture should be designed to handle the incoming data volume and ensure scalability and fault tolerance. Additionally, a machine learning model should be implemented to accurately detect anomalies in the energy usage patterns. This approach allows the system to process data in real-time, scale as needed, and respond to anomalies promptly, ensuring efficient energy management in the smart grid system.
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You are tasked with developing a stream processing solution for a smart grid system that monitors and manages energy consumption in real-time. The system needs to detect and respond to anomalies in energy usage patterns. How would you design the solution to achieve this?
A
Use a single centralized processing engine to handle all incoming data, without considering the scalability or fault tolerance.
B
Design a distributed processing architecture that can scale out based on the incoming data volume and detect anomalies using simple threshold-based rules.
C
Implement a machine learning model to detect anomalies in energy usage patterns, but do not consider real-time processing requirements.
D
Design a distributed processing architecture that can scale out based on the incoming data volume and implement a machine learning model to detect anomalies in real-time.