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Databricks Certified Data Engineer Professional Exam

Last Update Oct 16, 2025
Total Questions : 195 With Comprehensive Analysis

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Databricks Certified Data Engineer Professional Exam Questions and Answers

Questions 1

A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.

Which strategy will yield the best performance without shuffling data?

Options:

A.

Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.

B.

Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.

C.

Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.

D.

Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024/512), and then write to parquet.

E.

Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.

Questions 2

A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impression led to monitizable clicks.

Which solution would improve the performance?

A)

B)

C)

D)

Options:

A.

Option A

B.

Option B

C.

Option C

D.

Option D

Questions 3

A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.

One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.

What approach would allow them to do this?

Options:

A.

Maintain data quality rules in a Delta table outside of this pipeline’s target schema, providing the schema name as a pipeline parameter.

B.

Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.

C.

Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.

D.

Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.

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