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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer wants to refactor the following DLT code, which includes multiple table definitions with very similar code.

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for these tables.
How can the data engineer fix this?

A) Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
B) Wrap the for loop inside another table definition, using generalized names and properties to replace with those from the inner table definition.
C) Convert the list of configuration values to a dictionary of table settings, using table names as keys.
D) Move the table definition into a separate function, and make calls to this function using different input parameters inside the for loop.

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

3. 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 two.)

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

4. A data engineer needs to productionize a new Spark application written by teammate. This application has numerous external dependencies, including libraries, and requires custom environment variables and Spark configuration parameters to be set. Which two methods will help the data engineer accomplish the task? (Choose two.)

A) Use secrets in init scripts to store configuration data
B) Add libraries to compute policies
C) Use compute policies to set system properties, environment variables, and Spark configuration parameters.
D) Install libraries on DBFS
E) Create init scripts on DBFS.

5. A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

A) Cluster Event Log
B) Driver's log file
C) Workspace audit logs
D) Executor's log file
E) Ganglia

Solutions: