Optimize and Troubleshoot Data Storage and Data Processing DP-203 Practice Questions (Page 6)

Questions 26–30

1. 26application · medium

   A data engineering team runs an hourly Spark job that reads from a Delta table in Azure Data Lake Storage Gen2. The table receives thousands of small JSON files from an IoT device stream. The team notices that the job's read performance has degraded significantly over the past week. The cluster has sufficient memory and CPU. Which action should the team take first to improve read performance?

   • AEnable automatic Delta table caching.
   • BIncrease the number of executors on the cluster.
   • CRun OPTIMIZE on the Delta table to compact small files.
   • DSwitch the table format to Parquet without compaction.
   Reveal Solution Discussion Select an answer first
2. 27foundation · easy

   In Apache Spark, what does caching a DataFrame do?

   • AIt encrypts the DataFrame data for security.
   • BIt partitions the DataFrame to improve data distribution.
   • CIt stores the DataFrame in memory so that subsequent actions on it can be faster.
   • DIt writes the DataFrame to a permanent storage location.
   Reveal Solution Discussion Select an answer first
4. 28foundation · easy

   A Spark job processes a dataset partitioned by a 'country' column. One country contains 80% of the rows, causing some tasks to take much longer than others. What is this issue called?

   • AData replication
   • BData compaction
   • CData spill
   • DData skew
   Reveal Solution Discussion Select an answer first
5. 29application · medium

   An Azure Data Factory pipeline runs nightly. The pipeline copies data from an on-premises SQL Server to Azure Data Lake Storage Gen2. The copy activity fails with an error that the on-premises SQL Server connection timed out. The on-premises SQL Server is healthy and other applications can connect to it. What should you do first?

   • ASwitch the source to use a public endpoint instead of a private endpoint.
   • BChange the copy activity to use staging via Azure Blob Storage.
   • CCheck the integration runtime's network connectivity and firewall rules.
   • DIncrease the copy activity's concurrency and retry count.
   Reveal Solution Discussion Select an answer first
6. 30expert · hard

   A Spark job in Azure Databricks fails with 'java.lang.OutOfMemoryError: Java heap space' on the driver. The job reads a large number of small files and performs a wide transformation. The cluster has 8 workers with 32 GB memory each. The driver has 16 GB. You need to fix the failure. What should you do?

   • AIncrease the driver memory to 32 GB and reduce the number of partitions.
   • BCompact the small files into fewer, larger files before running the job.
   • CUse a broadcast join to avoid shuffling data to the driver.
   • DSet spark.sql.autoBroadcastJoinThreshold to -1 to disable broadcast joins.
   Reveal Solution Discussion Select an answer first