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

The following will be discussed here:

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

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

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Professional Data Engineer Exam Details

Like other Google exams, this exam also consists of multiple choice and multiple select questions. Consider the fact that you need to pay $200 for the registration. After that, you will access the test for 2 hours which is presented either in English or Japanese. Moreover, you can either take the exam online or have to find a test center near your place to take this test.

There is no formal prerequisite for the exam but it is recommended to have 3-4 years of experience within the data engineering field and to be responsible for the tasks related to data engineering and machine learning. So, on the final test day, you need to have exhaustive knowledge about these domains to perform your best.

• Providing solution quality
• Operationalizing machine learning models
• Building data processing systems
• Designing data processing systems

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