Course Content
This full-day instructor-led course helps prospective candidates structure their preparation for the Professional Data Engineer exam. The session covers the structure and format of the examination and its relationship to other Google Cloud certifications. Through lectures, quizzes, and discussions, candidates will familiarize themselves with the domain covered by the examination in order to devise a preparation strategy. Rehearses useful skills including exam question reasoning and case comprehension. Tips. Review of topics from the Data Engineering curriculum.
Who should attend
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
Prerequisites
To get the most out of this course, participants should:
- Familiarity with Google Cloud Platform to the level of the Data Engineering on Google Cloud Platform course (suggested, not required)
Course Objectives
- Position the Professional Data Engineer Certification
- Provide information, tips, and advice on taking the exam
- 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
Follow On Courses
Outline: Preparing for the Professional Data Engineer Examination (PPDEE)
Module 1
Understanding the Professional Data Engineer Certification
Topics Covered:
- 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
Designing Data Processing Systems
Topics Covered:
- Designing data processing systems
- Designing flexible data representations
- Designing data pipelines
- Designing data processing infrastructure
Module 3
Building and Operationalizing Data Processing Systems
Topics Covered:
- Building and maintaining data structures and databases
- Building and maintaining flexible data representations
- Building and maintaining pipelines
- Building and maintaining processing infrastructure
Module 4
Analyzing and Modeling (Review and preparation tips)
Topics Covered:
- 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
Module 5
Security, Policy, and Reliability
Topics Covered:
- 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
Module 6
Resources and Next Steps
Topics Covered:
- 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
Module 7
Resources and Next Steps
- Sample exam questions