Who is the Professional Data Engineer Exam Intended for?

This exam is designed for individuals who are experts in designing, building, securing, and monitoring data processing systems with a particular emphasis on compliance and security. The candidate who wants to take the Professional Data Engineer exam should have the ability to deploy, leverage, and training pre-existing machine learning models. Moreover, every applicant should have experience of more than 3 years including 1-year experience in designing and handling solutions utilizing GCP.

Google Professional Data Engineer Certified Professional salary

The average salary of a Google Professional Data Engineer Certified Expert in

• Europe - 135,347 EURO
• United State - 151,247 USD
• India - 25,42,327 INR
• England - 115,632 POUND

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

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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 Building and operationalizing data processing systems

The following will be discussed here:

• Storage costs and performance
• Transformation
• Building and operationalizing processing infrastructure
• Provisioning resources
• Building and operationalizing storage systems
• Awareness of current state and how to migrate a design to a future state
• Lifecycle management of data
• Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
• Adjusting pipelines
• Data acquisition and import
• Monitoring pipelines
• Validating a migration
• Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
• Testing and quality control
• Building and operationalizing pipelines
• Integrating with new data sources
• Building and operationalizing data processing systems
• Batch and streaming
• Data cleansing