Databricks Certified Professional Data Engineer - Databricks-Certified-Professional-Data-Engineer

A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context. Assuming the job is already deployed, they need to trigger its execution while ensuring the target-specific configuration is respected.
Which command will trigger the job execution?

• A. databricks execute my_project_job -e prod
• B. databricks job run my_project_job --env prod
• C. databricks run my_project_job -t prod
• D. databricks bundle run my_project_job -t prod

A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. A data engineer has been asked to ensure continuity: transfer catalog ownership to the platform team group, delegate ongoing privilege management, and retain the ability to receive and share data via Delta Sharing.
Which role must be in place to perform these actions across the metastore?

• A. Catalog Owner, because catalog owners can transfer any object in any catalog in the metastore.
• B. Metastore Admin, because metastore admins can transfer ownership and manage privileges across all metastore objects, including shares and recipients.
• C. Workspace Admin, because workspace admins can transfer ownership of any Unity Catalog object.
• D. Account Admin, because account admins can only create metastores but cannot change ownership of catalogs.

A data engineer inherits a Delta table with historical partitions by country that are badly skewed. Queries often filter by high-cardinality customer_id and vary across dimensions over time. The engineer wants a strategy that avoids a disruptive full rewrite, reduces sensitivity to skewed partitions, and sustains strong query performance as access patterns evolve.
Which two actions should the data engineer take? (Choose 2)

• A. Switch from static partitioning to liquid clustering and select initial clustering keys that reflect common filters such as customer_id.
• B. Periodically run OPTIMIZE table_name.
• C. Depend solely on optimized writes; Databricks will automatically replace partitioning with clustering over time.
• D. Keep existing partitions and rely on bin-packing OPTIMIZE only; ZORDER and clustering are unnecessary for multi-dimensional filters.
• E. Disable data skipping statistics to avoid maintenance overhead; rely on adaptive query execution instead.

A data engineer has created a new cluster using shared access mode with default configurations. The data engineer needs to allow the development team access to view the driver logs if needed.
What are the minimal cluster permissions that allow the development team to accomplish this?

• A. CAN RESTART
• B. CAN ATTACH TO
• C. CAN MANAGE
• D. CAN VIEW

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 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.
• B. 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.
• C. 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.
• D. 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.