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Certification Path

The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.

There is no prerequisite for this exam but still it would be best to follow some sequence in order to prove immense knowledge as a Google professional Data Engineer.

You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path

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

Who should take the Google Professional Data Engineer exam

Individuals should pursue the exam if they want to demonstrate their expertise and ability to design and develop Data Engineering. Following professional get benefited from Google Professional Data Engineer Certification

• Data engineers
• Developers responsible for managing big data transformation initiatives
• Data analysts
• Business analysts
• Data scientists
• Data architects

The Google Professional Data Engineer certification is designed to equip the individuals with the required knowledge and skills to enable data-driven decision-making through collecting, transforming, and publishing data. To earn this certificate, the candidates will be required to pass a single test measuring their skills in leveraging, deploying, and continuously training pre-existing machine learning models. The qualifying exam also evaluates the ability of the applicants to design, build, operationalize, monitor, and secure data processing systems.

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

The following will be discussed here:

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

Google Professional-Data-Engineer Exam Syllabus Topics: