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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

A data engineer, while designing a Pandas UDF to process financial time-series data with complex calculations that require maintaining state across rows within each stock symbol group, must ensure the function is efficient and scalable. Which approach will solve the problem with minimum overhead while preserving data integrity?

A. Use a SCALAR_ITER Pandas UDF with iterator-based processing, implementing state management through persistent storage (Delta tables) that gets updated after each batch to maintain continuity across iterator chunks.
B. Use a SCALAR Pandas UDF that processes the entire dataset at once, implementing custom partitioning logic within the UDF to group by stock symbol and maintain state using global variables shared across all executor processes.
C. Use a grouped_agg Pandas UDF that processes each stock symbol group independently, maintaining state through intermediate aggregation results that get passed between successive UDF calls via broadcast variables.
D. Use applyInPandas() on a Spark DataFrame that receives all rows for each stock symbol as a Pandas DataFrame, allowing processing within each group while maintaining state variables local to each group's processing function.

Question 2

The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When a database is being created, make sure that the LOCATION keyword is used.
B. When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.
C. When data is saved to a table, make sure that a full file path is specified alongside the Delta format.
D. When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.
E. When the workspace is being configured, make sure that external cloud object storage has been mounted.

Question 3

The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:

The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?

A. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
B. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
C. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
D. The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query
E. The source query failed to update properly for three consecutive minutes and then restarted

Question 4

A data engineer and a platform engineer are working together to automate their system tasks. A script needs to be executed outside of Databricks only if a particular daily Databricks job finishes successfully for the day. Databricks CLI command was used to check the last execution of the job. What are the required command options for that task?

A. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS --active- only
B. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS -- completed-only
C. databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- active-only
D. databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- completed-only

Question 5

The data engineer team is configuring environment for development testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team want to develop and test against similar production data as possible.
A junior data engineer suggests that production data can be mounted to the development testing environments, allowing pre production code to execute against production data. Because all users have Admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team.
Which statement captures best practices for this situation?

A. Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.
B. In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.
C. All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.
D. Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.

Solutions: