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A data engineer is using Lakeflow Declarative Pipelines Expectations feature to track the data quality...

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A data engineer is using Lakeflow Declarative Pipelines Expectations feature to track the data quality of their incoming sensor data. Periodically, sensors send bad readings that are out of range, and they are currently flagging those rows with a warning and writing them to the silver table along with the good data. They’ve been given a new requirement – the bad rows need to be quarantined in a separate quarantine table and no longer included in the silver table.

This is the existing code for their silver table:

@dlt.table

@dlt.expect( " valid_sensor_reading " , " reading < 120 " )

def silver_sensor_readings():

return spark.readStream.table( " bronze_sensor_readings " )

What code will satisfy the requirements?

A.

@dlt.table

@dlt.expect( " valid_sensor_reading " , " reading < 120 " )

def silver_sensor_readings():

return spark.readStream.table( " bronze_sensor_readings " )

@dlt.table

@dlt.expect( " invalid_sensor_reading " , " reading > = 120 " )

def quarantine_sensor_readings():

return spark.readStream.table( " bronze_sensor_readings " )

B.

@dlt.table

@dlt.expect_or_drop( " valid_sensor_reading " , " reading < 120 " )

def silver_sensor_readings():

return spark.readStream.table( " bronze_sensor_readings " )

@dlt.table

@dlt.expect( " invalid_sensor_reading " , " reading < 120 " )

def quarantine_sensor_readings():

return spark.readStream.table( " bronze_sensor_readings " )

C.

@dlt.table

@dlt.expect_or_drop( " valid_sensor_reading " , " reading < 120 " )

def silver_sensor_readings():

return spark.readStream.table( " bronze_sensor_readings " )

@dlt.table

@dlt.expect_or_drop( " invalid_sensor_reading " , " reading > = 120 " )

def quarantine_sensor_readings():

return spark.readStream.table( " bronze_sensor_readings " )

D.

@dlt.table

@dlt.expect_or_drop( " valid_sensor_reading " , " reading < 120 " )

def silver_sensor_readings():

return spark.readStream.table( " bronze_sensor_readings " )

@dlt.table

@dlt.expect( " invalid_sensor_reading " , " reading > = 120 " )

def quarantine_sensor_readings():

return spark.readStream.table( " bronze_sensor_readings " )

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