Crucial Exams

Microsoft Fabric Data Engineer Associate DP-700 Practice Question

You are designing a batch transformation in a Microsoft Fabric lakehouse. Sales information is stored in three normalized Delta tables named Orders, OrderLines, and Products. Analysts want a single wide table that contains one row per order line and all related product and order attributes to avoid run-time joins in Power BI. Which approach should you use to create the denormalized table while keeping ACID guarantees in the lakehouse?

  • Create a Fabric notebook that uses PySpark to join the Orders, OrderLines, and Products Delta tables and write the joined result as a new Delta table in the lakehouse.

  • Create an eventstream in Real-Time Intelligence to ingest the three tables and use KQL to materialize the joined output.

  • Use a pipeline copy activity to copy each source table into separate folders in the lakehouse and enable Auto Optimize on the folders.

  • Enable mirroring on the source database so the normalized schema is replicated automatically and rely on the default semantic model in Power BI.

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