Google Professional-Data-Engineer日本語 Exam Questions : Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)

  • Exam Code: Professional-Data-Engineer-JPN
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Updated: Aug 02, 2026
  • Q&As: 433 Questions and Answers

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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
  • Compute and Storage Fundamentals
  • Data Processing Architectures
  • Scaling Data Analytics
  • Machine Learning
  • Introducing Google Cloud Platform ‘

Reference: https://cloud.google.com/certification/data-engineer

Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality

The following will be discussed here:

  • Resizing and autoscaling resources
  • Ensuring scalability and efficiency
  • Pipeline monitoring (e.g., Stackdriver)
  • Designing for data and application portability (e.g., multi-cloud, data residency requirements)
  • Data security (encryption, key management)
  • Choosing between ACID, idempotent, eventually consistent 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))
  • Designing for security and compliance
  • Performing data preparation and quality control (e.g., Cloud Dataprep)
  • Ensuring reliability and fidelity
  • Identity and access management (e.g.,Cloud IAM)
  • Verification and monitoring
  • Data staging, cataloging, and discovery
  • Building and running test suites
  • Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
  • Ensuring flexibility and portability
  • Assessing, troubleshooting, and improving data representations and data processing infrastructure
  • Ensuring privacy (e.g., Data Loss Prevention API)
  • Mapping to current and future business requirements

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Google Professional-Data-Engineer日本語 Exam Syllabus Topics:

SectionWeightObjectives
Building and operationalizing data processing systems25%- Deploying and managing systems
  • 1. Monitoring and logging data processes
  • 2. Managing infrastructure and resources
- Building data pipelines
  • 1. Ingesting data from various sources
  • 2. Transforming and cleaning data
  • 3. Orchestrating data workflows
Operationalizing machine learning models20%- Preparing data for ML
  • 1. Feature engineering and data preparation
  • 2. Handling structured and unstructured data
- Deploying and maintaining ML models
  • 1. Model serving and monitoring
  • 2. Optimizing model performance and cost
Maintaining and automating data workloads18%- Resource optimization
  • 1. Choosing appropriate compute and storage options
  • 2. Cost management and resource allocation
- Automation and repeatability
  • 1. Implementing CI/CD for data systems
  • 2. Automating deployment and updates
Designing data processing systems20%- Designing for regulatory and security requirements
  • 1. Implementing access control and data protection
  • 2. Ensuring data privacy and compliance
- Designing for business requirements
  • 1. Designing for scalability and elasticity
  • 2. Selecting appropriate storage solutions
  • 3. Designing for reliability and fault tolerance
Ensuring solution quality and reliability17%- Troubleshooting and optimization
  • 1. Optimizing queries and workloads
  • 2. Diagnosing performance issues
- Testing and validating data systems
  • 1. Performance and scalability testing
  • 2. Data quality validation

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