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How to Prepare For Google Professional Data Engineer Exam
Preparation Guide for Google Professional Data Engineer Exam
Introduction to Google Professional Data Engineer Exam
Google has established a path for IT professionals endorse as a Data Engineer on the GCP platform. This accreditation program gives Google cloud professionals a way to endorse their skills. The evaluation relies on a meticulous exam using industry standard methodology to conclude whether or not a aspirant meets Google's proficiency standards.
The Professional Data Engineer exam assesses your ability to:
- Ensure solution quality
- Design data processing systems
- Operationalize machine learning models
- Build and operationalize data processing systems
Google Professional Data Engineer Exam certification is evidence of your skills, expertise in those areas in which you like to work. If candidate wants to work on Google Professional Data Engineer and prove his knowledge, Certification offered by Google. This Google Professional Data Engineer Certification helps a candidate to validates his skills in Big Data and Data engineering Technology.
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Training Courses Recommended for the Exam Preparation
Training courses are meant to help candidates to learn about the Google exam syllabus and prepare well. It has hands-on labs and expert support that will allow you to get in-depth knowledge of each domain covered in the test. So, these are some of the best training courses offered by Google for the Professional Data Engineer certification exam.
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Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality
The following will be discussed here:
- Data security (encryption, key management)
- 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 scalability and efficiency
- Designing for security and compliance
- Building and running test suites
- Assessing, troubleshooting, and improving data representations and data processing infrastructure
- Pipeline monitoring (e.g., Stackdriver)
- Designing for data and application portability (e.g., multi-cloud, data residency requirements)
- Resizing and autoscaling resources
- Verification and monitoring
- Ensuring flexibility and portability
- Identity and access management (e.g.,Cloud IAM)
- Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
- Mapping to current and future business requirements
- Choosing between ACID, idempotent, eventually consistent requirements
- Ensuring reliability and fidelity
- Ensuring privacy (e.g., Data Loss Prevention API)
- Performing data preparation and quality control (e.g., Cloud Dataprep)
- Data staging, cataloging, and discovery
Reference: https://cloud.google.com/certification/data-engineer
Google Professional-Data-Engineer日本語 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Operationalizing machine learning models | 26% | - ML pipeline integration
|
| Ensuring solution quality | 28% | - Reliability and performance
|
| Building and operationalizing data processing systems | 24% | - Data ingestion and integration
|
| Designing data processing systems | 22% | - Batch and streaming data processing design
|
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