Exam Overview
The Professional Data Engineer certification exam is a 2-hour test consisting of the multiple-choice and multiple-select questions. The students can take it in the English or Japanese languages. To register for and schedule the exam, you must pay the fee of $200. It is possible to sit for this test in an online proctored format at a remote location. You can also take it as an on-site proctored exam at a designated testing center.
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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
Reference: https://cloud.google.com/certification/data-engineer
This course is normally taken by data scientists, data analysts, and business analysts who are in the field of Google Сloud. It is a good way to prepare for your final exam because it teaches you all the details including 7 modules that cover all the Professional Data Engineer exam objectives:
- Data Analytics on the Cloud
- Additional Resources
- Data Processing Architectures
- Introducing Google Cloud Platform ‘
- Machine Learning
- Scaling Data Analytics
- Compute and Storage Fundamentals
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Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality
The following will be discussed here:
- Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
- Assessing, troubleshooting, and improving data representations and data processing infrastructure
- Ensuring scalability and efficiency
- Building and running test suites
- Data staging, cataloging, and discovery
- Mapping to current and future business requirements
- Ensuring privacy (e.g., Data Loss Prevention API)
- Designing for security and compliance
- Verification and monitoring
- Choosing between ACID, idempotent, eventually consistent requirements
- Designing for data and application portability (e.g., multi-cloud, data residency requirements)
- Ensuring reliability and fidelity
- Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
- Identity and access management (e.g.,Cloud IAM)
- Data security (encryption, key management)
- Ensuring flexibility and portability
- Resizing and autoscaling resources
- Performing data preparation and quality control (e.g., Cloud Dataprep)
- Pipeline monitoring (e.g., Stackdriver)
Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Ensuring solution quality and reliability | 17% | - Troubleshooting and optimization
|
| Topic 2: Designing data processing systems | 20% | - Designing for regulatory and security requirements
|
| Topic 3: Operationalizing machine learning models | 20% | - Preparing data for ML
|
| Topic 4: Maintaining and automating data workloads | 18% | - Resource optimization
|
| Topic 5: Building and operationalizing data processing systems | 25% | - Deploying and managing systems
|


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