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What is the duration, language, and format of Google Professional Data Engineer Exam
- Language: English (U.S.), Japanese, Spanish, and Portuguese
- Length of Examination: 120 minutes
- Cost: $200
- Passing score: 80%
- Format: Multiple choices, multiple answers
- Number of Questions: 50-60
Reference: https://cloud.google.com/certification/data-engineer
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:
- Predicting machine models using TensorFlow and Cloud ML
- Designing and building data processing systems on the Google Cloud Platform
- Processing batch and streaming data by using autoscaling data pipelines on Cloud Dataflow
- Enable insights from streaming data
- Influencing unstructured data using ML APIs on Cloud Dataproc
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The Professional Data Engineer exam is the industry-standard exam that proves the candidate’s ability to do data-driven decision-making by assembling, transforming, and publishing data. If you are rooting for a career in data engineering, you should take this test. It will lead you to attain the Professional Data Engineer certification issued by Google.
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Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Designing data processing systems | 20% | - Designing for business requirements
|
| Ensuring solution quality and reliability | 17% | - Troubleshooting and optimization
|
| Operationalizing machine learning models | 20% | - Preparing data for ML
|
| Maintaining and automating data workloads | 18% | - Automation and repeatability
|
| Building and operationalizing data processing systems | 25% | - Building data pipelines
|




