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Exam Code: Databricks-Certified-Professional-Data-Engineer Vendor: Databricks Name: Databricks Certified Data Engineer Professional Exam 202 Q&A with Explanation Last Update: May 27, 2026
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  • The Databricks-Certified-Professional-Data-Engineer Exam assesses an individual’s ability to use Databricks to perform advanced data engineering tasks, including building optimized and cleaned ETL pipelines, modeling data into a lakehouse, and ensuring that data pipelines are secure, reliable, monitored, and tested before deployment. The exam covers Databricks Tooling, Data Processing, Data Modeling, Security and Governance, Monitoring and Logging, and Testing and Deployment.
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Databricks Certified Data Engineer Professional Exam Questions and Answers

Question 1

The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.

Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

Options:

A.

Schedule a jo to execute the pipeline once and hour on a dedicated interactive cluster.

B.

Schedule a Structured Streaming job with a trigger interval of 60 minutes.

C.

Schedule a job to execute the pipeline once hour on a new job cluster.

D.

Configure a job that executes every time new data lands in a given directory.

Question 2

A data engineer is tasked with building a nightly batch ETL pipeline that processes very large volumes of raw JSON logs from a data lake into Delta tables for reporting. The data arrives in bulk once per day, and the pipeline takes several hours to complete. Cost efficiency is important, but performance and reliability of completing the pipeline are the highest priorities.

Which type of Databricks cluster should the data engineer configure?

Options:

A.

A lightweight single-node cluster with low worker node count to reduce costs.

B.

A high-concurrency cluster designed for interactive SQL workloads.

C.

An all-purpose cluster always kept running to ensure low-latency job startup times.

D.

A job cluster configured to autoscale across multiple workers during the pipeline run.

Question 3

Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?

Options:

A.

Regex

B.

Julia

C.

pyspsark.ml.feature

D.

Scala Datasets

E.

C++

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