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Databricks Databricks-Certified-Professional-Data-Engineer Exam With Confidence Using Practice Dumps

Exam Code:
Databricks-Certified-Professional-Data-Engineer
Exam Name:
Databricks Certified Data Engineer Professional Exam
Certification:
Vendor:
Questions:
127
Last Updated:
Sep 19, 2025
Exam Status:
Stable
Databricks Databricks-Certified-Professional-Data-Engineer

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Databricks Certified Data Engineer Professional Exam Questions and Answers

Question 1

The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.

Which of the following solutions addresses the situation while emphasizing simplicity?

Options:

A.

Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.

B.

Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.

C.

Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.

D.

Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.

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Question 2

A Delta Lake table representing metadata about content posts from users has the following schema:

user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE

This table is partitioned by the date column. A query is run with the following filter:

longitude < 20 & longitude > -20

Which statement describes how data will be filtered?

Options:

A.

Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.

B.

No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.

C.

The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.

D.

Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.

E.

The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.

Question 3

A data engineer wants to reflector the following DLT code, which includes multiple definition 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 tables.

How can the data engineer fix this?

Options:

A.

Convert the list of configuration values to a dictionary of table settings, using table names as keys.

B.

Convert the list of configuration values to a dictionary of table settings, using different input the for loop.

C.

Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.

D.

Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table