Crucial Exams

Microsoft Fabric Data Engineer Associate DP-700 Practice Question

You work in a Microsoft Fabric lakehouse. The Sales table has about 500 million rows, and the ProductSubcategory and ProductCategory tables each have fewer than 1 000 rows. You must build a daily Gold-layer table that denormalizes Sales with subcategory and category attributes while minimizing network shuffle and keeping the join in memory. Which Spark technique should you apply before running the joins?

  • Combine the three DataFrames with unionByName() and apply filters afterward.

  • Repartition the Sales DataFrame to a single partition, then perform the joins sequentially.

  • Disable Adaptive Query Execution so that Spark resorts to default shuffle hash joins.

  • Use the Spark broadcast() function (or BROADCAST join hint) on the two small lookup DataFrames before joining them to Sales.

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