Databricks-Certified-Professional-Data-Engineer Exam Questions - Navigate Your Path to Success

The Databricks Certified Data Engineer Professional (Databricks-Certified-Professional-Data-Engineer) exam is a good choice and if the candidate manages to pass Databricks Certified Data Engineer Professional exam, he/she will earn Databricks Data Engineer Professional Certification. Below are some essential facts for Databricks Databricks-Certified-Professional-Data-Engineer exam candidates:

TrendyCerts offers 120 Questions that are based on actual Databricks-Certified-Professional-Data-Engineer syllabus.
Our Databricks-Certified-Professional-Data-Engineer Exam Practice Questions were last updated on: Mar 03, 2025

Sample Questions for Databricks-Certified-Professional-Data-Engineer Exam Preparation

Question 1

A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.

Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table.

Given the current implementation, which method can be used?

AParse the Delta Lake transaction log to identify all newly written data files.

BExecute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.

CExecute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.

DParse the Spark event logs to identify those rows that were updated, inserted, or deleted.

Correct : C

Delta Lake provides built-in versioning and time travel capabilities, allowing users to query previous snapshots of a table. This feature is particularly useful for understanding changes between different versions of the table. In this scenario, where the table is overwritten nightly, you can use Delta Lake's time travel feature to execute a query comparing the latest version of the table (the current state) with its previous version. This approach effectively identifies the differences (such as new, updated, or deleted records) between the two versions. The other options do not provide a straightforward or efficient way to directly compare different versions of a Delta Lake table.

Delta Lake Documentation on Time Travel: Delta Time Travel

Delta Lake Versioning: Delta Lake Versioning Guide

Options Selected by Other Users:

Question 2

A data engineer is performing a join operating to combine values from a static userlookup table with a streaming DataFrame streamingDF.

Which code block attempts to perform an invalid stream-static join?

AuserLookup.join(streamingDF, ['userid'], how='inner')

BstreamingDF.join(userLookup, ['user_id'], how='outer')

CstreamingDF.join(userLookup, ['user_id''], how='left')

DstreamingDF.join(userLookup, ['userid'], how='inner')

EuserLookup.join(streamingDF, ['user_id'], how='right')

Correct : E

In Spark Structured Streaming, certain types of joins between a static DataFrame and a streaming DataFrame are not supported. Specifically, a right outer join where the static DataFrame is on the left side and the streaming DataFrame is on the right side is not valid. This is because Spark Structured Streaming cannot handle scenarios where it has to wait for new rows to arrive in the streaming DataFrame to match rows in the static DataFrame. The other join types listed (inner, left, and full outer joins) are supported in streaming-static DataFrame joins.

Structured Streaming Programming Guide: Join Operations

Databricks Documentation on Stream-Static Joins: Databricks Stream-Static Joins