Expert Answers to Databricks Exam Databricks-Certified-Professional-Data-Engineer Questions

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Databricks Certification Databricks Certified Data Engineer Professional Exam

Databricks Certified Data Engineer Professional Exam

Last Update Apr 26, 2026
Total Questions : 195

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Questions 2

A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df . The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.

Streaming DataFrame df has the following schema:

" device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT "

Code block:

Questions 2

Choose the response that correctly fills in the blank within the code block to complete this task.

Options:

to_interval( " event_time " , " 5 minutes " ).alias( " time " )

window( " event_time " , " 5 minutes " ).alias( " time " )

" event_time "

window( " event_time " , " 10 minutes " ).alias( " time " )

lag( " event_time " , " 10 minutes " ).alias( " time " )

Discussion 0

Questions 3

A data engineering team is migrating off its legacy Hadoop platform. As part of the process, they are evaluating storage formats for performance comparison. The legacy platform uses ORC and RCFile formats. After converting a subset of data to Delta Lake , they noticed significantly better query performance. Upon investigation, they discovered that queries reading from Delta tables leveraged a Shuffle Hash Join , whereas queries on legacy formats used Sort Merge Joins . The queries reading Delta Lake data also scanned less data.

Which reason could be attributed to the difference in query performance?

Options:

Delta Lake enables data skipping and file pruning using a vectorized Parquet reader.

The queries against the Delta Lake tables were able to leverage the dynamic file pruning optimization.

Shuffle Hash Joins are always more efficient than Sort Merge Joins.

The queries against the ORC tables leveraged the dynamic data skipping optimization but not the dynamic file pruning optimization.

Discussion 0

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Questions 4

Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?

Options:

In the Executor ' s log file, by gripping for " predicate push-down "

In the Stage ' s Detail screen, in the Completed Stages table, by noting the size of data read from the Input column

In the Storage Detail screen, by noting which RDDs are not stored on disk