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

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Target Audience

The candidates for this certification are the data engineers or those aiming to become one. These individuals should have the capacity to allow data-driven decision-making through the collection, transformation, and publishing of data. They have the expertise in designing, building, and operationalizing secure data processing systems and monitoring the same. This is with the specific emphasis on compliance and security, fidelity and reliability, portability and flexibility, as well as efficiency and scalability.

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

Google Professional-Data-Engineer Exam Syllabus Topics: