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Databricks Databricks-Certified-Professional-Data-Engineer Exam Questions
Certified Data Engineer Professional (Page 6 )

Updated On: 21-Feb-2026
Page 6 of 37
PDF with 327 Questions

A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day. At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.

Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?

  1. Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle executors to begin processing the next batch while longer running tasks from previous batches finish.
  2. Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum execution time observed for each batch is always best practice to ensure no records are dropped.
  3. The trigger interval cannot be modified without modifying the checkpoint directory; to maintain the current stream state, increase the number of shuffle partitions to maximize parallelism.
  4. Use the trigger once option and configure a Databricks job to execute the query every 10 seconds; this ensures all backlogged records are processed with each batch.
  5. Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.

Answer(s): E



Which statement describes Delta Lake Auto Compaction?

  1. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an OPTIMIZE job is executed toward a default of 1 GB.
  2. Before a Jobs cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
  3. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
  4. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
  5. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an OPTIMIZE job is executed toward a default of 128 MB.

Answer(s): E



Which statement characterizes the general programming model used by Spark Structured Streaming?

  1. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
  2. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
  3. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
  4. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
  5. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.

Answer(s): D



Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

  1. spark.sql.files.maxPartitionBytes
  2. spark.sql.autoBroadcastJoinThreshold
  3. spark.sql.files.openCostInBytes
  4. spark.sql.adaptive.coalescePartitions.minPartitionNum
  5. spark.sql.adaptive.advisoryPartitionSizeInBytes

Answer(s): A



A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

  1. Task queueing resulting from improper thread pool assignment.
  2. Spill resulting from attached volume storage being too small.
  3. Network latency due to some cluster nodes being in different regions from the source data
  4. Skew caused by more data being assigned to a subset of spark-partitions.
  5. Credential validation errors while pulling data from an external system.

Answer(s): D






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