Google Professional Data Engineer Certified Professional salary
The average salary of a Google Professional Data Engineer Certified Expert in
- Europe - 135,347 EURO
- England - 115,632 POUND
- United State - 151,247 USD
- India - 25,42,327 INR
Reference: https://cloud.google.com/certification/data-engineer
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Certification Path
The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.
There is no prerequisite for this exam but still it would be best to follow some sequence in order to prove immense knowledge as a Google professional Data Engineer.
You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path
Operationalizing Machine Learning Models
Here the candidates need to demonstrate their expertise in using pre-built Machine Learning models as a service, including Machine Learning APIs (for instance, Speech API, Vision API, etc.), customizing Machine Learning APIs (for instance, Auto ML text, AutoML Vision, etc.), conversational experiences (for instance, Dialogflow). The applicants should also have the skills in deploying the Machine Learning pipeline. This involves the ability to ingest relevant data, perform retraining of machine learning models (BigQuery ML, Cloud Machine Learning Engine, Spark ML, Kubeflow), as well as execute continuous evaluation. Additionally, the students should be able to choose the relevant training & serving infrastructure as well as know how to fulfill measuring, monitoring, and troubleshooting of Machine Learning models.
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Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Designing data processing systems | 20% | - Designing for regulatory and security requirements
|
| Ensuring solution quality and reliability | 17% | - Testing and validating data systems
|
| Maintaining and automating data workloads | 18% | - Automation and repeatability
|
| Building and operationalizing data processing systems | 25% | - Building data pipelines
|
| Operationalizing machine learning models | 20% | - Preparing data for ML
|

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