Crucial Exams

Microsoft Fabric Data Engineer Associate DP-700 Practice Question

A nightly Microsoft Fabric pipeline loads a Parquet file to a bronze folder and then upserts data into a silver Delta Lake table named Customers. The file may repeat customer_id values because of late updates or replayed files. You need the silver table to keep only the newest updated_at row per customer_id and allow safe re-runs without new duplicates. Which approach should you use?

  • A Spark notebook that reads the file, writes it to the Customers Delta table in append mode, and then runs OPTIMIZE ZORDER BY (updated_at).

  • A Spark notebook that executes a Delta Lake MERGE INTO Customers USING the nightly DataFrame ON customer_id, updating the row only when the incoming updated_at value is greater and inserting otherwise.

  • A Spark notebook that calls dropDuplicates("customer_id") on the DataFrame and overwrites the Customers table on each load.

  • A Data Factory copy activity that writes the file to the lakehouse with the preserveHierarchy option set to true and skipDuplicates enabled.

Microsoft Fabric Data Engineer Associate DP-700
Ingest and transform data
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