Preparing for the Professional Data Engineer Examination

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Duration

1 Days

6 CPD hours

This course is intended for

This course is intended for the following participants:Cloud professionals interested in taking the Data Engineer certification exam.Data engineering professionals interested in taking the Data Engineer certification exam.

Overview

This course teaches participants the following skills:
Position the Professional Data Engineer Certification
Provide information, tips, and advice on taking the exam
Review the sample case studies
Review each section of the exam covering highest-level concepts sufficient to build confidence in what is known by the candidate and indicate skill gaps/areas of study if not known by the candidate
Connect candidates to appropriate target learning

Description

This course will help prospective candidates plan their preparation for the Professional Data Engineer exam. The session will cover the structure and format of the examination, as well as its relationship to other Google Cloud certifications. Through lectures, quizzes, and discussions, candidates will familiarize themselves with the domain covered by the examination, to help them devise a preparation strategy. Rehearse useful skills including exam question reasoning and case comprehension. Tips and review of topics from the Data Engineering curriculum.

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
Sample Case Studies for the Professional Data Engineer Exam
  • Flowlogistic
  • MJTelco
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
Analyzing and Modeling (Review and preparation tips)
  • 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
Reliability, Policy, and Security (Review and preparation tips)
  • Design for reliability
  • Performing quality control
  • Assessing, troubleshooting, and improving data representation and data processing infrastructure
  • Recovering data
  • Visualize data and advocate policy
  • Building (or selecting) data visualization 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
Resources and next steps
  • 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
Training Insurance Included!

When you organise training, we understand that there is a risk that some people may fall ill, become unavailable. To mitigate the risk we include training insurance for each delegate enrolled on our public schedule, they are welcome to sit on the same Public class within 6 months at no charge, if the case arises.

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