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Answer: Leverage the built-in 'WHERE' clause to apply a range filter based on the expected temperature values.
Option D, leveraging the built-in 'WHERE' clause to apply a range filter, is the most effective approach for cleansing the data in real-time. By specifying a range filter based on the expected temperature values, you can filter out any readings that fall outside of this range, effectively removing invalid or corrupted data points. This approach allows for real-time data cleansing without the need for batch processing or custom deserialization logic.
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You are working on a data cleansing project using Azure Stream Analytics. You have a stream of sensor data that includes temperature readings from multiple sensors. Some of the readings are incorrect or corrupted, and you need to filter out these invalid data points. Which of the following approaches would be most effective for cleansing the data in real-time?
A
Use a batch processing approach and store the data in a temporary storage before cleansing.
B
Implement a custom deserialization logic to handle the corrupted data points.
C
Use the built-in 'IS NULL' condition in the SELECT statement to filter out null or missing values.
D
Leverage the built-in 'WHERE' clause to apply a range filter based on the expected temperature values.
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