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

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

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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:

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

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.

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