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Professional Data Engineer Exam Details
Like other Google exams, this exam also consists of multiple choice and multiple select questions. Consider the fact that you need to pay $200 for the registration. After that, you will access the test for 2 hours which is presented either in English or Japanese. Moreover, you can either take the exam online or have to find a test center near your place to take this test.
There is no formal prerequisite for the exam but it is recommended to have 3-4 years of experience within the data engineering field and to be responsible for the tasks related to data engineering and machine learning. So, on the final test day, you need to have exhaustive knowledge about these domains to perform your best.
- Designing data processing systems
- Operationalizing machine learning models
- Providing solution quality
- Building data processing systems
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.
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What is the duration, language, and format of Google Professional Data Engineer Exam
- Number of Questions: 50-60
- Passing score: 80%
- Language: English (U.S.), Japanese, Spanish, and Portuguese
- Length of Examination: 120 minutes
- Format: Multiple choices, multiple answers
- Cost: $200
Reference: https://cloud.google.com/certification/data-engineer
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Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Maintaining and automating data workloads | 18% | - Resource optimization
|
| Topic 2: Building and operationalizing data processing systems | 25% | - Deploying and managing systems
|
| Topic 3: Ensuring solution quality and reliability | 17% | - Testing and validating data systems
|
| Topic 4: Operationalizing machine learning models | 20% | - Preparing data for ML
|
| Topic 5: Designing data processing systems | 20% | - Designing for business requirements
|
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