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Training Courses Recommended for the Exam Preparation
Training courses are meant to help candidates to learn about the Google exam syllabus and prepare well. It has hands-on labs and expert support that will allow you to get in-depth knowledge of each domain covered in the test. So, these are some of the best training courses offered by Google for the Professional Data Engineer certification exam.
Data Engineering on Google Cloud course
It is a 4-day course that gives hands-on experience to the candidates and allows them to build data processing systems on Google Cloud. It will also show you how to design data processing systems, analyze data and build end-to-end data pipelines and machine learning. In order to get a better understanding of the course, you need to complete the big data machine learning course or get equivalent experience. This course also aids you in developing applications using a programming language such as Python and covers the following objective:
- Designing and building data processing systems on the Google Cloud Platform
- Influencing unstructured data using ML APIs on Cloud Dataproc
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- Predicting machine models using TensorFlow and Cloud ML
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Google Professional Data Engineer Practice Test Questions, Google Professional Data Engineer Exam Practice Test Questions
The Google Professional Data Engineer certification is designed to evaluate the candidates’ skills in designing data processing systems and ensuring solution quality. It is also created to measure their competence in building and operationalizing data processing systems and operationalizing ML models. The potential applicants must complete a single exam to get certified.
Reference: https://cloud.google.com/certification/data-engineer
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Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operationalizing machine learning models | 20% | - Preparing data for ML
|
| Topic 2: Designing data processing systems | 20% | - Designing for business requirements
|
| Topic 3: Ensuring solution quality and reliability | 17% | - Testing and validating data systems
|
| Topic 4: Maintaining and automating data workloads | 18% | - Resource optimization
|
| Topic 5: Building and operationalizing data processing systems | 25% | - Building data pipelines
|
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