Pass Professional-Data-Engineer Exam Latest Practice Questions Updated on Oct 06, 2022
Google Professional-Data-Engineer Study Guide Archives
Ensure Solution Quality
- Ensure Portability & Flexibility: The considerations for this domain include the design for application and data portability, including data residency prerequisites and Multiple-Cloud. It also coves data staging, discovery, and cataloging, as well as mapping to future and current business prerequisites.
- Ensure Fidelity & Reliability: The applicants should be able to carry out data preparation & quality control (such as Cloud Dataprep), verify and monitor, as well as plan, execute, and stress test data recovery (including rerunning failed jobs, fault tolerance, and retrospective re-analysis performance). Besides that, they should be able to choose between idempotent ACID and eventual consistent prerequisites;
- Design for Compliance & Security: The consideration for this topic includes identity & access management such as Cloud IAM. You should also know about data security (including key management and encryption) and privacy assurance (such as Data Loss Prevention API). This part also covers the skills needed in legal compliance, including Health Insurance Portability & Accountability Act, FedRAMP, Children’s Online Privacy Protection Act, and General Data Protection Regulation;
- Ensure Efficiency & Scalability: The potential candidates will be required to demonstrate their ability to build and run test suits as well as monitor pipeline, including Stackdriver. It also focuses on their skills related to assessing, improving, and troubleshooting data process infrastructure and data representations. This area will also require that the test takers demonstrate the capacity to resize and autoscale resources;
[WATU 223]
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)
- Ensuring reliability and fidelity
- Resizing and autoscaling resources
- Data security (encryption, key management)
- Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
- Verification and monitoring
- Data staging, cataloging, and discovery
- Assessing, troubleshooting, and improving data representations and data processing infrastructure
- Designing for security and compliance
- Ensuring scalability and efficiency
- Choosing between ACID, idempotent, eventually consistent requirements
- Ensuring privacy (e.g., Data Loss Prevention API)
- Designing for data and application portability (e.g., multi-cloud, data residency requirements)
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