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Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

Which Python variable contains a list of directories to be searched when trying to locate required modules?

A. pylib.source
B. importlib.resource path
C. pypi.path
D. sys.path
E. os.path

Question 2

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

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

Question 3

A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

A. Date
B. Post_id
C. User_id
D. latitude
E. Post_time

Question 4

Which statement describes the correct use of pyspark.sql.functions.broadcast?

A. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
B. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.
C. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
D. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
E. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.

Question 5

A data engineer is brining an existing production Databricks job under asset bundle management and wants to ensure that:
- The job's current configuration is captured as YAML, and all
referenced files are included in their bundle project.
- Future changes to the bundle's YAML will update the existing job in-
place (not create a new job)
How should the data engineer successfully move the production job under asset bundle management?

A. Run databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deployment, bind to link the bundle's job resource to the existing job in Databricks.
B. Manually create the YAML configuration for the job in your bundle project, ensuring all settings match the existing job. Then, run Databricks bundle deploy the bundle, which will update the existing job in your workspace.
C. Run Databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deploy to deploy the bundle, which will always update the existing job automatically.
D. Export the job definition as JSON, convert it to YAML, and place it in your bundle. Then, run Databricks bundle deploy to update the existing job.

Solutions: