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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.

Actual questions ensure 100% passing

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This course will show you how to manage big data including loading, extracting, cleaning, and validating data. At the end of the training, you can easily create machine learning and statistical models as well as visualizing query results. This program is a bit lengthy but you have to practice well to get the knowledge needed on the actual exam. These are the following modules covered in the course:

• Creating a Data Lake
• Prebuilt ML Models APIs for Unsaturated Data
• Building a Data Warehouse
• Custom Model building Using SQL in BigQuery ML
• Introduction to Building Batch Data Pipelines
• Bigtable Streaming Features and High-Throughput BigQuery
• Introduction to Data Engineering
• Introduction to Processing Streaming Data
• Cloud Dataflow Streaming Features
• Serverless Messaging Using Cloud Sub/Pub
• Custom Model building Utilizing Cloud AutoML
• Advanced BigQuery Performance and Functionality
• Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
• Production ML Pipelines and use of Kubeflow
• Serverless Data Processing with Cloud Dataflow
• Big Data Analytics with Cloud Al Platform Notebook
• Performing Spark on Cloud Dataproc

These modules involve everything the candidate requires for passing the Professional Data Engineer certification exam. Thus, you will not miss anything if you are taking this learning program keenly and apply the required knowledge in an appropriate way. You would end up getting a good score and achieving the Google Professional Data Engineer certification.

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

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