Databricks

Nhan_Nguyen · ‎01-31-2023

Hi team,

I'm going to take the Databricks Certified Data Engineer Professional exam.

It's really hard. Could anyone give me some materials or resources.

I'm already taken the preparation course on customer.databricks but seem it's not enough knowlege.

Thanks

Aviral-Bhardwaj · ‎01-31-2023

only academy courses thats it

Cami · ‎02-02-2023

you can find more info under the link https://community.databricks.com/s/question/0D58Y00009XisLhSAJ/data-engineering-professional-practic...

jose_gonzalez · ‎02-23-2023

Hi @Nhan Nguyen,

Just a friendly follow-up. Did any of the responses help you to resolve your question? if it did, please mark it as best. Otherwise, please let us know if you still need help.

rockper · ‎05-28-2023

There are two courses in Databricks Academy. They are bundled in the Data Engineer Learning Plan.

Data Engineering with Databricks v3 (12 hours) for DE Associate
Advanced Data Engineering with Databricks (also 12 hours) for DE Professional

The first course also has extensive slides and code.

Try to attend a Certification Overview either as a live Webinar or as a course in DB Academy. It covers the certification topics more granularly. There are also prep courses and practice tests on Udemy

KaKa · ‎07-16-2023

Hi Guy,

go to customer-academy.databrick.com and search key work :

" Certification Overview: Databricks Certified Data Engineer Professional Exam ID: E-VMWOJ1"

This course will cover the format and structure of the exam, topics assessed by the exam, example questions, and tips for exam preparation.

Learning objectives

Describe the learning context, format, and structure behind the exam.
Describe the topics covered in the exam.
Recognize the different types of questions provided on the exam.
Identify resources that can be used to learn the material covered in the exam.

Prerequisites

Describe how to use and the benefits of the Databricks platform and developer tools
Build optimized and cleaned data processing pipelines using the Spark and Delta Lake APIs
Model data into a Lakehouse using knowledge of general data modeling concepts
Ensure data pipelines secure, reliable, monitored, and tested before deployment