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.

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

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

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

2
  • Sample Case Studies for the Professional Data Engineer Exam

  • Flowlogistic
    MJTelco

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

4
  • 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

5
  • 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

6
  • 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

Audience

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

Language

English

Prerequisites

To get the most out of this course, participants should have: Familiarity with Google Cloud Platform to the level of the Data Engineering on Google Cloud Platform course (suggested, not required)

$675

Length: 1.0 day (8 hours)

Level:

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