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Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality
The following will be discussed here:
- Ensuring flexibility and portability
- Performing data preparation and quality control (e.g., Cloud Dataprep)
- Ensuring scalability and efficiency
- Resizing and autoscaling resources
- Choosing between ACID, idempotent, eventually consistent requirements
- Mapping to current and future business requirements
- Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
- Building and running test suites
- Ensuring reliability and fidelity
- Verification and monitoring
- Data staging, cataloging, and discovery
- Ensuring privacy (e.g., Data Loss Prevention API)
- Designing for data and application portability (e.g., multi-cloud, data residency requirements)
- Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
- Designing for security and compliance
- Assessing, troubleshooting, and improving data representations and data processing infrastructure
- Data security (encryption, key management)
- Pipeline monitoring (e.g., Stackdriver)
- Identity and access management (e.g.,Cloud IAM)
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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.
Reference: https://cloud.google.com/certification/data-engineer
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Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Ensuring solution quality and reliability | 17% | - Testing and validating data systems
|
| Topic 2: Building and operationalizing data processing systems | 25% | - Deploying and managing systems
|
| Topic 3: Maintaining and automating data workloads | 18% | - Resource optimization
|
| Topic 4: Operationalizing machine learning models | 20% | - Preparing data for ML
|
| Topic 5: Designing data processing systems | 20% | - Designing for regulatory and security requirements
|
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