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

The following will be discussed here:

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

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

Exam Overview

The Professional Data Engineer certification exam is a 2-hour test consisting of the multiple-choice and multiple-select questions. The students can take it in the English or Japanese languages. To register for and schedule the exam, you must pay the fee of $200. It is possible to sit for this test in an online proctored format at a remote location. You can also take it as an on-site proctored exam at a designated testing center.

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