Crucial Exams

Microsoft Fabric Data Engineer Associate DP-700 Practice Question

You manage a Microsoft Fabric lakehouse that contains a Delta table named Transactions with 500 million rows added each month. For reporting, you must create a nightly process that produces a summary table with total sales amount and order count for every customer and month. The solution must use PySpark, minimize shuffle-related memory usage, and store the result as a managed Delta table for downstream queries. Which approach meets the requirements?

  • Call df.groupBy("customerId").pivot("sale_month").sum("amount").write.format("delta").mode("overwrite").saveAsTable("SalesMonthly") without partitioning.

  • Convert the table to an RDD, use map and reduceByKey to calculate sums and counts, then save the results as a single CSV file.

  • Use df.groupBy("customerId", "sale_month").agg(sum_("amount").alias("total_amount"), count("*").alias("order_count")).write.format("delta").partitionBy("customerId", "sale_month").mode("overwrite").saveAsTable("SalesMonthly")

  • Run a Spark SQL statement that uses GROUPING SETS to aggregate the data, then write the output in Parquet format without partitions.

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