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  • Exam Code: Professional-Data-Engineer日本語
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Q & A: 403 Questions and Answers
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  • Exam Code: Professional-Data-Engineer日本語
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Q & A: 403 Questions and Answers
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  • Exam Code: Professional-Data-Engineer日本語
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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:

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

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

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Data Engineering on Google Cloud course

It is a 4-day course that gives hands-on experience to the candidates and allows them to build data processing systems on Google Cloud. It will also show you how to design data processing systems, analyze data and build end-to-end data pipelines and machine learning. In order to get a better understanding of the course, you need to complete the big data machine learning course or get equivalent experience. This course also aids you in developing applications using a programming language such as Python and covers the following objective:

  • Enable insights from streaming data
  • Processing batch and streaming data by using autoscaling data pipelines on Cloud Dataflow
  • Influencing unstructured data using ML APIs on Cloud Dataproc
  • Predicting machine models using TensorFlow and Cloud ML
  • Designing and building data processing systems on the Google Cloud Platform

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