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

The following will be discussed here:

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

Google Professional-Data-Engineer Exam Syllabus Topics: