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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

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Professional Data Engineer Exam Details

Like other Google exams, this exam also consists of multiple choice and multiple select questions. Consider the fact that you need to pay $200 for the registration. After that, you will access the test for 2 hours which is presented either in English or Japanese. Moreover, you can either take the exam online or have to find a test center near your place to take this test.

There is no formal prerequisite for the exam but it is recommended to have 3-4 years of experience within the data engineering field and to be responsible for the tasks related to data engineering and machine learning. So, on the final test day, you need to have exhaustive knowledge about these domains to perform your best.

• Operationalizing machine learning models
• Designing data processing systems
• Building data processing systems
• Providing solution quality

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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:

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

Google Professional-Data-Engineer日本語 Exam Syllabus Topics: