Crucial Exams

Microsoft Fabric Data Engineer Associate DP-700 Practice Question

A data engineer schedules a Microsoft Fabric notebook from a Data Factory pipeline. The notebook loads 200 GB of JSON files into a Delta table by using the following code in the first cell:

(spark.read
      .option("multiline", "true")
      .json("/lake/raw/transactions")
      .write
      .format("delta")
      .mode("overwrite")
      .saveAsTable("finance.transactions_raw"))

When the pipeline runs, the notebook fails after several minutes. The run details page for the notebook activity shows the following error message:

java.lang.OutOfMemoryError: Java heap space

You need to modify the notebook so that the next execution succeeds without changing the pipeline definition or the underlying capacity. Which change should you make?

  • Add .coalesce(1) after spark.read to consolidate the data before writing.

  • Call repartition(200) on the DataFrame before writing it.

  • Persist the DataFrame with .persist() before writing it to the Delta table.

  • Insert spark.conf.set("spark.sql.shuffle.partitions", "8") at the start of the notebook.

Microsoft Fabric Data Engineer Associate DP-700
Monitor and optimize an analytics solution
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