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How to book Google Professional Data Engineer Exams

The registration for the Google Professional Data Engineer Exam follows the steps given below.

• Step1: Visit the Google Cloud Webassessor Website
• Step4: Take the date of the exam, choose exam center and make further payment using payment method like credit/debit etc.
• Step3: Search for the exam name Google Professional Data Engineer
• Step2: Sign in or sign up to your Google Cloud Webassessor account

Preparation Process

To perform well in the Google Professional Data Engineer certification exam, the candidates must be ready to devote ample time to preparation. There is a host of study materials available on the Internet, but if you want to be confident in the authenticity of the resources that you use, it is best to refer to the official platform. Google recommends that the applicants follow the Professional Data Engineer learning path, which is a comprehensive option involving in-person classes, online training, hands-on labs, and other resources from Google Cloud.

Besides that, it is recommended that the students use the official sample questions to familiarize themselves with the question formats that they will encounter during the actual exam. The official webpage also contains additional resources such as Google Cloud documentation and Google Cloud solutions. There is also an option of joining the subject-related webinar to get valuable preparation tips from the Google experts.

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

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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:

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

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