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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:
Databricks Certification
Vendor:
Databricks
Questions:
195
Last Updated:
Apr 26, 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

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Question 1

Which statement describes the correct use of pyspark.sql.functions.broadcast?

Options:

A.

It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.

B.

It marks a column as small enough to store in memory on all executors, allowing a broadcast join.

C.

It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.

D.

It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.

E.

It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.

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Question 2

A table named user_ltv is being used to create a view that will be used by data analysis on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.

The user_ltv table has the following schema:

An analyze who is not a member of the auditing group executing the following query:

Which result will be returned by this query?

Options:

A.

All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.

B.

All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.

C.

All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.

D.

All records from all columns will be displayed with the values in user_ltv.

Question 3

Given the following PySpark code snippet in a Databricks notebook:

filtered_df = spark.read.format( " delta " ).load( " /mnt/data/large_table " ) \

.filter( " event_date > ' 2024-01-01 ' " )

filtered_df.count()

The data engineer notices from the Query Profiler that the scan operator for filtered_df is reading almost all files, despite the filter being applied.

What is the probable reason for poor data skipping?

Options:

A.

The Delta table lacks optimization that enables dynamic file pruning.

B.

The filter is executed only after the full data scan, preventing data skipping.

C.

The event_date column is outside the table’s partitioning and Z-ordering scheme.

D.

The filter condition involves a data type excluded from data skipping support.

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