The Google Cloud Certified Professional Data Engineer certification is a globally recognized credential that honed your abilities in making data-driven choices by maximizing the acquired data. The certification programme effectively demonstrates how to gather, process, and analyses data in order to provide relevant insights. This thorough course intends to provide practical understanding of how to design, create, manage, and debug data processing systems, with an emphasis on the system’s critical elements such as dependability, scalability, fault-tolerance, fidelity, security, and efficiency.

A Data Engineer is responsible for performing data analysis to predict future business outcomes, developing statistical models to assist decision-making, and developing machine learning models to simplify and automate major business operations.

Exam Format and Information

Exam for Google Professional Data Engineer

A Professional Data Engineer collects, transforms, and publishes data to allow data-driven decision making. A Data Engineer candidate should be able to design, implement, operationalize, protect, and monitor data processing systems with a focus on security and compliance, scalability and efficiency, dependability and fidelity, and flexibility and portability.

Exam Name

Google Cloud Certified Professional Data Engineer

Exam Duration

120 Minutes

Exam Type

Multiple Choice Examination

Passing Score

No Scoring Criteria

Exam Fee





2 Years

Exam Languages

English, Japanese, Spanish, Portuguese

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Course Description

Let’s go over the exam outline after we’ve gotten a better understanding of the necessary details.

  • First and foremost, appropriate storage technologies must be chosen.
  • Following that, create designs for data pipelines.
  • Third, create a data processing solution.
  • Finally, data warehousing and data processing must be migrated.
  • First and foremost, storage systems must be constructed and operationalized.
  • Following that, pipelines with processing infrastructure will be built and operationalized.
  • To begin, using pre-built ML models as a service.
  • Then, an ML pipeline is deployed.
  • Third, select the proper training and serving infrastructure.
  • Finally, machine learning models must be measured, monitored, and troubleshooter.
  • First, consider security and compliance.
  • Then, with dependability and fidelity, ensure scalability and efficiency.
  • Finally, ensure portability and flexibility.


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