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Google Professional Data Engineer Certified Professional salary
The average salary of a Google Professional Data Engineer Certified Expert in
- United State - 151,247 USD
- England - 115,632 POUND
- Europe - 135,347 EURO
- India - 25,42,327 INR
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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:
- Introduction to Processing Streaming Data
- Prebuilt ML Models APIs for Unsaturated Data
- Cloud Dataflow Streaming Features
- Creating a Data Lake
- Handling Data Pipelines with Cloud Composer and Cloud Data Fusion
- Building a Data Warehouse
- Serverless Messaging Using Cloud Sub/Pub
- Performing Spark on Cloud Dataproc
- Big Data Analytics with Cloud Al Platform Notebook
- Production ML Pipelines and use of Kubeflow
- Custom Model building Utilizing Cloud AutoML
- Introduction to Building Batch Data Pipelines
- Advanced BigQuery Performance and Functionality
- Introduction to Data Engineering
- Bigtable Streaming Features and High-Throughput BigQuery
- Custom Model building Using SQL in BigQuery ML
- 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.
Difficulty in Attempting Google Professional Data Engineer Exam Certification
If the user has successfully passed the professional-data-engineer practice exam and has been through professional-data-engineer exam dumps then the certification exam will not be too much difficult as the user has shown aptitude for understanding complicated processes.
Reference: https://cloud.google.com/certification/data-engineer
Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Preparing and using data for analysis (~15% of the exam) | 15% | - Sharing data securely
|
| Topic 2: Maintaining and automating data workloads (~15% of the exam) | 15% | - Designing for reliability and fidelity
|
| Topic 3: Designing data processing systems (~30% of the exam) | 30% | - Designing data processing resources
|
| Topic 4: Storing the data (~20% of the exam) | 20% | - Designing for a data platform
|
| Topic 5: Ingesting and processing the data (~20% of the exam) | 20% | - Deploying and operationalizing the pipelines
|
