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Understanding functional and technical aspects of Google Professional Data Engineer Exam Building and operationalizing data processing systems

The following will be discussed here:

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

Build & Operationalize Data Processing Systems

• Build & Operationalize Processing Infrastructure: The considerations for this subject area include provisioning resources, adjusting pipeline, monitoring pipeline, and testing & quality control.
• Build & Operationalize Pipeline: This module requires that the learners demonstrate competence in data cleansing, transformation, batch & streaming, data import & acquisition, as well as integration with the new data sources;
• Build & Operationalize Storage Systems: This part will require the students’ skills and competence in the effective usage of managed services, including Cloud Spanner, CLoug Bigtable, BigQuery, Cloud SQL, Cloud Memorystore, Cloud Datastore, and Cloud Storage. It also covers their skills in managing the data lifecycle and storage performance and costs;

Operationalizing Machine Learning Models

Here the candidates need to demonstrate their expertise in using pre-built Machine Learning models as a service, including Machine Learning APIs (for instance, Speech API, Vision API, etc.), customizing Machine Learning APIs (for instance, Auto ML text, AutoML Vision, etc.), conversational experiences (for instance, Dialogflow). The applicants should also have the skills in deploying the Machine Learning pipeline. This involves the ability to ingest relevant data, perform retraining of machine learning models (BigQuery ML, Cloud Machine Learning Engine, Spark ML, Kubeflow), as well as execute continuous evaluation. Additionally, the students should be able to choose the relevant training & serving infrastructure as well as know how to fulfill measuring, monitoring, and troubleshooting of Machine Learning models.

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