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Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality

The following will be discussed here:

• Data staging, cataloging, and discovery
• Identity and access management (e.g.,Cloud IAM)
• Ensuring flexibility and portability
• Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
• Ensuring scalability and efficiency
• Verification and monitoring
• Resizing and autoscaling resources
• Choosing between ACID, idempotent, eventually consistent requirements
• Ensuring privacy (e.g., Data Loss Prevention API)
• Mapping to current and future business requirements
• Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
• Pipeline monitoring (e.g., Stackdriver)
• Assessing, troubleshooting, and improving data representations and data processing infrastructure
• Building and running test suites
• Designing for data and application portability (e.g., multi-cloud, data residency requirements)
• Ensuring reliability and fidelity
• Performing data preparation and quality control (e.g., Cloud Dataprep)
• Designing for security and compliance
• Data security (encryption, key management)

Reference: https://cloud.google.com/certification/data-engineer

This course is normally taken by data scientists, data analysts, and business analysts who are in the field of Google Сloud. It is a good way to prepare for your final exam because it teaches you all the details including 7 modules that cover all the Professional Data Engineer exam objectives:

• Data Processing Architectures
• Machine Learning
• Additional Resources
• Scaling Data Analytics
• Data Analytics on the Cloud
• Introducing Google Cloud Platform ‘
• Compute and Storage Fundamentals