Preparing for the Professional Data Engineer Examination

Gain valuable information, tips, and advice to help you prepare for Google Cloud data engineer certification.
1 day course
Supporting material
Virtual, Private
Virtual Classroom
A convenient and interactive learning experience, that enables you to attend on of our courses from the comfort of your own home or anywhere you can log on. We offer Virtual Classroom on selected live classroom courses where this will appear as an option under the location drop down if available. These can also be booked as Private Virtual Classrooms for exclusive business sessions.
A private training session for your team. Groups can be of any size, at a location of your choice including our training centres.

Jellyfish has been selected by Google to facilitate the delivery of this one-day instructor led course. As a Google Cloud Partner, we’re here to help structure your preparation for the Professional Data Engineer exam.

We'll cover the structure and format of the examination, as well as its relationship to other Google Cloud certifications.

Through in-classroom discussions and quizzes, you will familiarise yourself with the domain covered by the examination, so as to help you devise a preparation strategy.

Together, we’ll rehearse useful skills including exam question reasoning and case comprehension, as well as review topics from the Data Engineering curriculum.

This Preparing for the Professional Data Engineer Examination course is offered as a private training session and can be delivered at our own training facilities in San Francisco or Baltimore, any location of your choice or via Virtual Classroom.

Course overview
Who should attend:
This course is intended for Cloud or Data engineering professionals interested in taking the Data Engineer certification exam. To get the most out of this course, it’s recommended that you have familiarity with the Google Cloud Platform to the level of the Data Engineering on Google Cloud Platform course.
Walk away with the ability to:
  • Provide information, tips, and advice on taking the exam
  • In-depth review of sample case studies
  • Review each section of the exam covering highest-level concepts sufficient to build confidence in what is known by the candidate
  • Indicate skill gaps/areas of study if not known by the candidate
  • Connect candidates to appropriate target learning
Course agenda
Module 1: Understanding the Professional Data Engineer Certification
  • 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
Module 2: Sample Case Studies for the Professional Data Engineer Exam
  • Flowlogistic
  • MJTelco
Module 3: Designing and Building (Review and preparation tips)
  • Designing data processing systems
  • Designing flexible data representations
  • Designing data pipelines
  • Designing data processing infrastructure
  • Build and maintain data structures and databases
  • Building and maintaining flexible data representations
  • Building and maintaining pipelines
  • Building and maintaining processing infrastructure
Module 4: Analysing and Modeling (Review and preparation tips)
  • Analyse data and enable machine learning
  • Analysing data
  • Machine learning
  • Machine learning model deployment
  • Model business processes for analysis and optimisation
  • Mapping business requirements to data representations
  • Optimising data representations, data infrastructure performance and cost
Module 5: Reliability, Policy, and Security (Review and preparation tips)
  • Performing quality control
  • Assessing, troubleshooting, and improving data representation and data processing infrastructure
  • Recovering data
  • Visualise data and advocate policy
  • Building (or selecting) data visualisation and reporting tools
  • Advocating policies and publishing data and reports
  • Design for security and compliance
  • Designing secure data infrastructure and processes
  • Designing for legal compliance
Module 6: Resources and next steps
  • Designing data processing systems, data structures, and databases
  • Data analysis, machine learning, business process analysis, and optimisation
  • Data visualisation and policy Resources for learning more about reliability design
  • Business process analysis and optimisation
  • Reliability, policies, security, and compliance
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