Exam Details
The Google Professional Data Engineer certification exam has the duration of 2 hours. The qualifying test is made up of multiple-select and multiple-choice questions. The exam is available either in Japanese or English. To register for it, you are required to go through the official webpage and pay the fee of $200 plus applicable taxes. While completing the registration process, the potential individuals can choose the preferred method of exam delivery. It can be taken in person at the nearest testing center or online from a remote location.
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Requirements
The certification does not have any official prerequisites. However, it is advised to have at least three years of industry experience with one or more years of expertise in designing and managing different solutions with the use of Google Cloud Platform. It is also required to review the topics of the qualifying exam before sitting for it.
Reference: https://cloud.google.com/certification/data-engineer
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Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Ensuring solution quality and reliability | 17% | - Troubleshooting and optimization
- 1. Optimizing queries and workloads
- 2. Diagnosing performance issues
- Testing and validating data systems
- 1. Performance and scalability testing
- 2. Data quality validation
|
| Maintaining and automating data workloads | 18% | - Automation and repeatability
- 1. Implementing CI/CD for data systems
- 2. Automating deployment and updates
- Resource optimization
- 1. Cost management and resource allocation
- 2. Choosing appropriate compute and storage options
|
| Building and operationalizing data processing systems | 25% | - Building data pipelines
- 1. Orchestrating data workflows
- 2. Transforming and cleaning data
- 3. Ingesting data from various sources
- Deploying and managing systems
- 1. Monitoring and logging data processes
- 2. Managing infrastructure and resources
|
| Designing data processing systems | 20% | - Designing for regulatory and security requirements
- 1. Implementing access control and data protection
- 2. Ensuring data privacy and compliance
- Designing for business requirements
- 1. Designing for reliability and fault tolerance
- 2. Designing for scalability and elasticity
- 3. Selecting appropriate storage solutions
|
| Operationalizing machine learning models | 20% | - Deploying and maintaining ML models
- 1. Optimizing model performance and cost
- 2. Model serving and monitoring
- Preparing data for ML
- 1. Feature engineering and data preparation
- 2. Handling structured and unstructured data
|