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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:
195
Last Updated:
Jan 13, 2026
Exam Status:
Stable
Databricks Databricks-Certified-Professional-Data-Engineer

Databricks-Certified-Professional-Data-Engineer: Databricks Certification Exam 2025 Study Guide Pdf and Test Engine

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Databricks Certified Data Engineer Professional Exam Questions and Answers

Question 1

A Delta Lake table was created with the below query:

Consider the following query:

DROP TABLE prod.sales_by_store -

If this statement is executed by a workspace admin, which result will occur?

Options:

A.

Nothing will occur until a COMMIT command is executed.

B.

The table will be removed from the catalog but the data will remain in storage.

C.

The table will be removed from the catalog and the data will be deleted.

D.

An error will occur because Delta Lake prevents the deletion of production data.

E.

Data will be marked as deleted but still recoverable with Time Travel.

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Question 2

Which distribution does Databricks support for installing custom Python code packages?

Options:

A.

sbt

B.

CRAN

C.

CRAM

D.

nom

E.

Wheels

F.

jars

Question 3

The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.

Which statement is a possible explanation for this behavior?

Options:

A.

%sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.

B.

Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.

C.

%sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.

D.

Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.

E.

%sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.