Debugging and Troubleshooting DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   What is the purpose of parameter overrides in a Databricks job run?

   • ATo bypass the job's permissions
   • BTo permanently change the job's default parameters
   • CTo override the cluster's Spark configuration
   • DTo adjust job configurations for a specific run without modifying the job definition
   Reveal Solution Discussion Select an answer first
2. 12application · medium

   A data engineer is troubleshooting a Spark job that fails intermittently with a `java.lang.OutOfMemoryError` in the executor logs. The job processes a large shuffle and the Spark UI shows high shuffle read/write metrics. Which action should the engineer take FIRST to diagnose the root cause?

   • AReview the driver log for the stack trace of the OutOfMemoryError.
   • BIncrease the `spark.executor.memory` setting and rerun the job immediately.
   • CCheck the Spark UI's Executors tab for per-executor memory usage and shuffle spill metrics.
   • DQuery the `system.compute.cluster_events` table to see if the cluster was resized during the run.
   Reveal Solution Discussion Select an answer first
4. 13expert · hard

   A Lakeflow Spark Declarative Pipeline is running slowly. The Spark UI shows a stage with high task duration and high shuffle read. The pipeline event logs show that the stage completed successfully. Which is the most likely cause of the slow performance?

   • AData skew in the join operation causing some tasks to process much more data.
   • BThe pipeline is using too many clusters, causing resource contention.
   • CThe pipeline's configuration has a misconfigured timeout.
   • DThe event logs are not capturing the performance metrics.
   Reveal Solution Discussion Select an answer first
5. 14expert · hard

   A Lakeflow Spark Declarative Pipeline fails during a table update, but the event logs show no error. The Spark UI shows a failed stage with a `Task not serializable` exception. Which is the most likely reason the event logs do not show the error?

   • AThe event logs capture pipeline-level events, but not task-level exceptions from the Spark execution engine.
   • BThe event logs are only updated after the pipeline completes successfully.
   • CThe event logs are stored in a different region and are not accessible.
   • DThe event logs are truncated due to a large number of events.
   Reveal Solution Discussion Select an answer first
6. 15foundation · easy

   When using the Repair feature on a failed job run, which of the following can you modify before rerunning?

   • AThe job's source code
   • BThe job's permissions
   • CThe job's schedule
   • DThe cluster configuration
   Reveal Solution Discussion Select an answer first