Microsoft Fabric Data Engineer Associate DP-700 Practice Question
You are building a daily load from an on-premises SQL Server table into a Delta Lake table in a Microsoft Fabric lakehouse. The source table contains hundreds of millions of rows and has an incrementing bigint column named UpdateSequence that increases each time a row is inserted or modified. Loads must copy only new and changed rows, overwrite any existing versions in the destination, and be fully restartable without creating duplicate rows. Which loading pattern meets these requirements?
Implement a watermark-based incremental load that filters rows where UpdateSequence is greater than the last stored watermark, then upsert the data into the Delta Lake table by using MERGE INTO.
Schedule a nightly pipeline that truncates the Delta Lake table and performs a full bulk INSERT of the entire source table.
Configure two alternating pipelines: the first copies rows whose UpdateSequence modulo 2 equals 0, and the second copies the remaining rows the next day.
Use an eventstream to capture change events and append them directly to the Delta Lake table without performing updates.
A watermark-based incremental load keeps track of the highest UpdateSequence value that was successfully loaded. During each run, the pipeline filters the source by selecting rows with an UpdateSequence greater than this watermark, minimizing the volume of data copied. The pipeline then executes a MERGE INTO statement on the Delta Lake table, which updates existing rows and inserts new ones in an idempotent manner. Because the watermark is updated only after the MERGE finishes, a failed run can be safely retried without introducing duplicates. Truncating and reloading the entire table wastes resources. Simply appending rows or dividing the table by an arbitrary modulo would not update changed records and could still duplicate data.
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Microsoft Fabric Data Engineer Associate DP-700
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