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Google Professional-Data-Engineer Exam Overview:
| Certification Vendor: | Google Cloud |
|---|---|
| Exam Name: | Google Cloud Professional Data Engineer Exam |
| Exam Number: | Professional-Data-Engineer |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 40 - 50 |
| Exam Format: | Multiple select, Multiple choice |
| Related Certifications: | Google Cloud Professional Cloud Architect Google Cloud Associate Cloud Engineer Google Cloud Professional Data Analyst |
| Certificate Validity Period: | 2 years |
| Exam Price: | USD 200 (plus tax where applicable) |
| Passing Score: | 700 / 1000 |
| Available Languages: | Japanese, English |
| Recommended Training: | Official Exam Guide Google Cloud Professional Data Engineer Learning Path |
| Exam Registration: | Google Cloud Certification Registration |
| Sample Questions: | Google Professional-Data-Engineer Sample Questions |
| Exam Way: | Online-proctored or onsite-proctored |
| Pre Condition: | No mandatory prerequisites; recommended 3+ years industry experience, including 1+ year designing and managing Google Cloud data solutions |
| Official Syllabus URL: | https://cloud.google.com/learn/certification/data-engineer |
Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality
The following will be discussed here:
- Data staging, cataloging, and discovery
- Pipeline monitoring (e.g., Stackdriver)
- Designing for data and application portability (e.g., multi-cloud, data residency requirements)
- Identity and access management (e.g.,Cloud IAM)
- Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
- Ensuring privacy (e.g., Data Loss Prevention API)
- Performing data preparation and quality control (e.g., Cloud Dataprep)
- Ensuring reliability and fidelity
- Data security (encryption, key management)
- Ensuring flexibility and portability
- Resizing and autoscaling resources
- Mapping to current and future business requirements
- Choosing between ACID, idempotent, eventually consistent requirements
- Building and running test suites
- Assessing, troubleshooting, and improving data representations and data processing infrastructure
- Designing for security and compliance
- Verification and monitoring
- Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
- Ensuring scalability and efficiency
Target Audience
The candidates for this certification are the data engineers or those aiming to become one. These individuals should have the capacity to allow data-driven decision-making through the collection, transformation, and publishing of data. They have the expertise in designing, building, and operationalizing secure data processing systems and monitoring the same. This is with the specific emphasis on compliance and security, fidelity and reliability, portability and flexibility, as well as efficiency and scalability.
Reference: https://cloud.google.com/certification/data-engineer
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.
Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Operationalizing machine learning models | 20% | - Deploying and maintaining ML models
|
| Building and operationalizing data processing systems | 25% | - Deploying and managing systems
|
| Designing data processing systems | 20% | - Designing for regulatory and security requirements
|
| Maintaining and automating data workloads | 18% | - Automation and repeatability
|
| Ensuring solution quality and reliability | 17% | - Testing and validating data systems
|
By Venus
