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Databricks Certified Data Engineer Professional Sample Questions:

1. A developer has successfully configured their credentials for Databricks Repos and cloned a remote Git repository. They do not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace. Which approach allows this user to share their code updates without the risk of overwriting the work of their teammates?

A) Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository
B) Use repos to merge all difference and make a pull request back to the remote repository.
C) Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
D) Use Repos to merge all differences and make a pull request back to the remote repository.
E) Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.

2. 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?

A) Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
B) Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), 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) 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.
E) Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.

3. Why are Pandas UDFs often preferred over traditional PySpark UDFs in performance-critical applications involving large datasets?

A) They eliminate the JVM-Python boundary by bypassing serialization entirely, thereby avoiding data conversion overhead.
B) They allow row-level execution of functions in Python with native Spark optimization, removing the need for columnar execution.
C) They minimize memory usage by streaming each row individually through a lightweight Python wrapper, avoiding batch processing overhead.
D) They leverage Apache Arrow to enable vectorized operations between the JVM and Python runtimes, reducing serialization costs and improving computational efficiency.

4. Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?

A) Regex
B) Scala Datasets
C) pyspsark.ml.feature
D) C++
E) Julia

5. In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both deep and shallow clone, development tables are created using shallow clone. A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that vacuum was run the day before.
Why are the cloned tables no longer working?

A) Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.
B) The data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.
C) Running vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.
D) Tables created with SHALLOW CLONE are automatically deleted after their default retention threshold of 7 days.
E) The metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command

Solutions: