1 day course 
Partner of the Year 
Private Course Credits
Select the pre-paid training investment that’s right for you and help your money stretch a little further with our course credits.
As a Google Cloud Partner, we’re here to help you prepare for the Professional Data Engineer exam.
In this session, we'll cover the structure and format of the examination, as well as its relationship to other Google Cloud certifications.
Together, we’ll rehearse useful skills including exam question reasoning and case comprehension, as well as review topics from the Data Engineering curriculum. Through in-classroom discussions and quizzes, you will familiarize yourself with the domain covered by the examination and plan out a study strategy.
Our Preparing for the Professional Data Engineer Examination course is available as a private training session that can be delivered via Virtual Classroom or at a location of your choice in the US.
Course overview
Who should attend:
This course is for Cloud or data engineering professionals interested in taking the Data Engineer certification exam. We recommend that you’re familiar with Google Cloud and have taken the Data Engineering on Google Cloud course.
What you'll learn:
By the end of this course, you will be able to:
- Make use of information, tips, and advice on taking the exam
- Utilize sample case studies
- Review each section of the exam covering highest-level concepts
- Identify skill gaps / areas of study that you need to spend more time on
- Access appropriate target learning
Course agenda
- 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 operationalizing data structures and databases
- Building and operationalizing flexible data representations
- Building and operationalizing pipelines
- Building and operationalizing processing infrastructure
- Analyze data and enable machine learning
- Analyzing data
- Machine learning
- Machine learning model deployment
- Model business processes for analysis and optimization
- Mapping business requirements to data representations
- Optimizing data representations, data infrastructure performance and cost
- Design for security and compliance
- Performing quality control
- Ensuring reliability
- Visualize data and advocate policy
- Assessing, troubleshooting, and improving data representations and data processing infrastructure
- Recovering data
- Building (or selecting) data visualization and reporting tools
- Advocating policies and publishing data and reports
- Designing secure data infrastructure and processes
- Designing for legal compliance
- Resources for learning more about designing data processing systems, data structures, and databases
- Resources for learning more about data analysis, machine learning, business process analysis, and optimization
- Resources for learning more about data visualization and policy
- Resources for learning more about reliability design
- Resources for learning more about business process analysis and optimization
- Resources for learning more about reliability, policies, security, and compliance
- Sample exam questions