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

Databricks Certified Data Engineer Professional Sample Questions:

1. The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.

Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A) No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
B) Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
C) Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
D) No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
E) No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.

2. A data governance team at a large enterprise is improving data discoverability across its organization. The team has hundreds of tables in their Databricks Lakehouse with thousands of columns that lack proper documentation. Many of these tables were created by different teams over several years, with missing context about column meanings and business logic. The data governance team needs to quickly generate comprehensive column descriptions for all existing tables to meet compliance requirements and improve data literacy across the organization. They want to leverage modern capabilities to automatically generate meaningful descriptions rather than manually documenting each column, which would take months to complete. Which approach should the team use in Databricks to automatically generate column comments and descriptions for existing tables?

A) Write custom PySpark code using df.describe() and df.schema to programmatically generate basic statistical descriptions for each column.
B) Navigate to the table in Databricks Catalog Explorer, select the table schema view, and use the AI Generate option which leverages artificial intelligence to automatically create meaningful column descriptions based on column names, data types, sample values, and data patterns.
C) Use the DESCRIBE TABLE command to extract existing schema information and manually write descriptions based on column names and data types.
D) Use Delta Lake's DESCRIBE HISTORY command to analyze table evolution and infer column purposes from historical changes.

3. In a Databricks Asset Bundle project, in the file resources/app.yml, the data engineer would like to deploy a Databricks Apps databricks_app_deployed and Volume volume_deployed and grant the Service Principal behind Databricks Apps permissions to READ and WRITE to the Volume.
How should the data engineer achieve the deployment?

A)

B)

C)

D)

4. The data science team has created and logged a production model using MLflow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, loads the customers table containing the customer_id key column into a DataFrame, and defines the feature columns needed for the model.

Which code block will output a DataFrame with the schema "customer_id LONG, predictions DOUBLE"?

A) model.predict(df, columns)
B) df.apply(model, columns).select("customer_id, predictions")
C) df.select("customer_id", pandas_udf(model, columns).alias("predictions"))
D) df.map(lambda x:model(x[columns])).select("customer_id, predictions")
E) df.select("customer_id", model(*columns).alias("predictions"))

5. A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

A) Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
B) Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
C) Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
D) Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
E) Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.

Solutions: