Microsoft Fabric Data Engineer Associate DP-700 Practice Question
Your organization ingests daily CSV sales files into a Delta table in Microsoft Fabric by using a Dataflow Gen2. Some days, the upstream system resends one or more previous files, causing duplicate rows in the target table. You must modify the solution so that reprocessing the same file does not create duplicates and avoids a full table reload. What should you do?
Enable the Auto Compaction feature for the Delta table.
Add a distinct transformation on all columns in the dataflow before the sink.
Replace the dataflow sink with a Fabric notebook that executes a Delta MERGE statement matching on TransactionID.
Change the dataflow to load the files into a new table each day and use UNION ALL to combine the tables.
Executing a Delta MERGE operation allows you to perform idempotent upserts. The MERGE statement compares incoming rows to the existing Delta Lake table on a business key such as TransactionID; matching rows are updated and non-matching rows are inserted. Because the command is executed every load, duplicates introduced by retransmitted files are eliminated without rewriting the whole table. A distinct transformation inside the dataflow would only remove duplicates within each individual batch, not across historical data. Auto-compaction optimises small files but does not remove logical duplicates. Loading into a new table every day and using UNION ALL merely propagates the duplicates rather than removing them.
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Why is using UNION ALL not effective for removing duplicates in Dataflow Gen2?
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Microsoft Fabric Data Engineer Associate DP-700
Ingest and transform data
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