Target Audience
The candidates for this certification are the data engineers or those aiming to become one. These individuals should have the capacity to allow data-driven decision-making through the collection, transformation, and publishing of data. They have the expertise in designing, building, and operationalizing secure data processing systems and monitoring the same. This is with the specific emphasis on compliance and security, fidelity and reliability, portability and flexibility, as well as efficiency and scalability.
Reference: https://cloud.google.com/certification/data-engineer
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Who should take the Google Professional Data Engineer exam
Individuals should pursue the exam if they want to demonstrate their expertise and ability to design and develop Data Engineering. Following professional get benefited from Google Professional Data Engineer Certification
- Data scientists
- Business analysts
- Developers responsible for managing big data transformation initiatives
- Data architects
- Data analysts
- Data engineers
This course is normally taken by data scientists, data analysts, and business analysts who are in the field of Google Сloud. It is a good way to prepare for your final exam because it teaches you all the details including 7 modules that cover all the Professional Data Engineer exam objectives:
- Introducing Google Cloud Platform ‘
- Scaling Data Analytics
- Data Processing Architectures
- Data Analytics on the Cloud
- Additional Resources
- Machine Learning
- Compute and Storage Fundamentals
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Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building and operationalizing data processing systems | 25% | - Building data pipelines
|
| Topic 2: Maintaining and automating data workloads | 18% | - Resource optimization
|
| Topic 3: Operationalizing machine learning models | 20% | - Preparing data for ML
|
| Topic 4: Designing data processing systems | 20% | - Designing for regulatory and security requirements
|
| Topic 5: Ensuring solution quality and reliability | 17% | - Testing and validating data systems
|
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