Preparing for the Professional Data Engineer exam

The purpose of this course is to help qualified people develop the confidence to take the exam and to help those who are not yet qualified to develop a preparation plan.


In this course, participants will learn the following skills:
  • Positioning of the Professional Data Engineer certification
  • Information, tips and suggestions for taking the exam.
  • Review of case study examples
  • Review of each section of the exam that covers concepts at a level sufficient to generate confidence in the candidate’s knowledge and indicate gaps in skills and other areas of study, if unknown
  • Appropriate learning resources for each candidate.


This class is aimed at the following audience:
  • Individuals preparing for the Professional Data Engineer examination.


To make the most of this course, participants must meet the following criteria:

  • Have knowledge and experience with Google Cloud equivalent to the Data Engineering course
    (recommended, not mandatory)


8 hours (1 day)


Check the next open public class in our enrollment page.
If you are interested in a private training class for your company, contact-us.
Preparing for the Professional Data Engineer Exam dependencies with other courses and certifications
Preparing for the Professional Data Engineer Exam dependencies with other courses and certifications

Course Outline

The course includes presentations, demonstrations and hands-on labs.
  • Position the Professional Data Engineer certification among the offerings
  • Distinguish between Associate and Professional
  • Provide guidance between Professional Data Engineer and Associate Cloud Engineer
  • Describe how the exam is administered and the exam rules
  • Provide general advice about taking the exam
  • Designing data processing systems
  • Designing flexible data representations
  • Designing data pipelines
  • Designing data processing infrastructure
  • Building and maintaining data structures and databases
  • Building and maintaining flexible data representations
  • Building and maintaining pipelines
  • Building and maintaining processing infrastructure
  • Analyzing data and enabling machine learning
  • Deploying an ML pipeline
  • Machine learning terminology review
  • Operationalizing Machine Learning Models: Exam Guide Review
  • Modeling business processes for analysis and optimization
  • Designing for security and compliance
  • Performing quality control
  • Ensuring reliability
  • Visualizing data and advocating policy
  • Ensuring Solution Quality: Exam Guide Review
  • Debrief
  • Preparation Resources