Expert Answers to Databricks Exam Databricks-Certified-Professional-Data-Engineer Questions

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Databricks Certification Databricks Certified Data Engineer Professional Exam

Databricks Certified Data Engineer Professional Exam

Last Update Jan 5, 2026
Total Questions : 195

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Questions 2

The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.

A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:

Questions 2

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.

Which statement explains the cause of this failure?

Options:

Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.

The activity details table already exists; CHECK constraints can only be added during initial table creation.

The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.

The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.

The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.

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Questions 3

The data science team has created and logged a production 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, load the customer table containing the customer_id key column into a Dataframe, and defines the feature columns needed for the model.

Questions 3

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

Options:

Model, predict (df, columns)

Df, map (lambda k:midel (x [columns]) ,select (''customer_id predictions'')

Df. Select (''customer_id''.

Model (''columns) alias (''predictions'')

Df.apply(model, columns). Select (''customer_id, prediction''

Discussion 0

Questions 4

A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.

The user attempts and fails to accomplish this by adding an expectation to the report table definition.

Which approach would allow using DLT expectations to validate all expected records are present in this table?

Options:

Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.

Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table

Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null

Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table

Discussion 0

Questions 5

A data team's Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.

Questions 5