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Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality
The following will be discussed here:
- Pipeline monitoring (e.g., Stackdriver)
- Assessing, troubleshooting, and improving data representations and data processing infrastructure
- Identity and access management (e.g.,Cloud IAM)
- Verification and monitoring
- Building and running test suites
- Designing for data and application portability (e.g., multi-cloud, data residency requirements)
- Resizing and autoscaling resources
- Ensuring reliability and fidelity
- Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
- Performing data preparation and quality control (e.g., Cloud Dataprep)
- Data security (encryption, key management)
- Designing for security and compliance
- Choosing between ACID, idempotent, eventually consistent requirements
- 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
- Data staging, cataloging, and discovery
- Ensuring privacy (e.g., Data Loss Prevention API)
- Mapping to current and future business requirements
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.
- Designing data processing systems
- Operationalizing machine learning models
- Providing solution quality
- Building data processing systems
