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You are building a solution that will detect anomalies in sensor data from the previous 24 hours. You need to ensure that the solution scans the entire dataset, at the same time, for anomalies. Which type of detection should you use?
Correct Answer: A
Batch anomaly detection is a type of anomaly detection that scans the entire dataset at once for outliers and unusual patterns. Batch anomaly detection is suitable for offline analysis of historical data, such as sensor data from the previous 24 hours. Batch anomaly detection can use various techniques, such as statistical methods, machine learning methods, or hybrid methods, to identify anomalies in the data123.