Microsoft Fabric Data Engineer Associate DP-700 Practice Question
A Fabric Eventstream ingests JSON telemetry from Azure Event Hubs and writes the data to a Delta table in a Lakehouse. After a device firmware update, the incoming payload now contains an additional field named "pressure". In the Eventstream monitoring pane you notice that events are being dropped with a "column count mismatch" error on the Lakehouse output. You must allow the new field to be stored without losing events or recreating the table. Which action should you take?
Enable the Auto create table option on the Lakehouse output so the table is regenerated with the new column.
Run a Spark SQL ALTER TABLE command in the Lakehouse to add a "pressure" column with the appropriate data type.
Modify the Eventstream transformation to exclude the "pressure" field from the SELECT statement.
Increase the Lakehouse output batch interval to give the service more time to process larger events.
The Lakehouse Delta table schema must match the list of columns produced by the Eventstream SELECT statement. When a new field appears in the incoming data, the Eventstream continues to forward it, but the Lakehouse write fails if the target table lacks that column. Adding the new column to the existing table with an ALTER TABLE statement brings the schemas back into alignment. Auto-create only works when no table exists, removing the column would discard required data, and changing the batch interval does not affect schema mismatches.
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Microsoft Fabric Data Engineer Associate DP-700
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