Preparation Process
To perform well in the Google Professional Data Engineer certification exam, the candidates must be ready to devote ample time to preparation. There is a host of study materials available on the Internet, but if you want to be confident in the authenticity of the resources that you use, it is best to refer to the official platform. Google recommends that the applicants follow the Professional Data Engineer learning path, which is a comprehensive option involving in-person classes, online training, hands-on labs, and other resources from Google Cloud.
Besides that, it is recommended that the students use the official sample questions to familiarize themselves with the question formats that they will encounter during the actual exam. The official webpage also contains additional resources such as Google Cloud documentation and Google Cloud solutions. There is also an option of joining the subject-related webinar to get valuable preparation tips from the Google experts.
Reference: https://cloud.google.com/certification/data-engineer
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Certification Path
The Google Professional Data Engineer Certification is one of the highest level of certification mainly focussing to the professional Data Engineering.
There is no prerequisite for this exam but still it would be best to follow some sequence in order to prove immense knowledge as a Google professional Data Engineer.
You can complete Google Associate Certifications then approach for the professional certification. For more information related to Google cloud certification track Google-certification-path
Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Ensuring solution quality and reliability | 17% | - Testing and validating data systems
- 1. Data quality validation
- 2. Performance and scalability testing
- Troubleshooting and optimization
- 1. Diagnosing performance issues
- 2. Optimizing queries and workloads
|
| Topic 2: 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
|
| Topic 3: Building and operationalizing data processing systems | 25% | - Deploying and managing systems
- 1. Monitoring and logging data processes
- 2. Managing infrastructure and resources
- Building data pipelines
- 1. Transforming and cleaning data
- 2. Ingesting data from various sources
- 3. Orchestrating data workflows
|
| Topic 4: Operationalizing machine learning models | 20% | - Preparing data for ML
- 1. Handling structured and unstructured data
- 2. Feature engineering and data preparation
- Deploying and maintaining ML models
- 1. Model serving and monitoring
- 2. Optimizing model performance and cost
|
| Topic 5: Designing data processing systems | 20% | - Designing for regulatory and security requirements
- 1. Ensuring data privacy and compliance
- 2. Implementing access control and data protection
- Designing for business requirements
- 1. Designing for scalability and elasticity
- 2. Designing for reliability and fault tolerance
- 3. Selecting appropriate storage solutions
|