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

  • Exam Code: Professional-Data-Engineer日本語
  • Exam Name: Google Certified Professional Data Engineer Exam (Professional-Data-Engineer日本語版)
  • Updated: Sep 17, 2026
  • Q & A: 433 Questions and Answers

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What is the duration, language, and format of Google Professional Data Engineer Exam

  • Passing score: 80%
  • Cost: $200
  • Length of Examination: 120 minutes
  • Language: English (U.S.), Japanese, Spanish, and Portuguese
  • Format: Multiple choices, multiple answers
  • Number of Questions: 50-60

Ensuring Solution Quality

The last section of the certification exam evaluates the ability of the learners to design for security & compliance, including identity & access management, legal compliance, data security, and privacy ensuring. Moreover, they should be able to ensure flexibility & portability, reliability & fidelity, as well as scalability & efficiency.

The Professional Data Engineer exam is the industry-standard exam that proves the candidate’s ability to do data-driven decision-making by assembling, transforming, and publishing data. If you are rooting for a career in data engineering, you should take this test. It will lead you to attain the Professional Data Engineer certification issued by Google.

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

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The world has witnessed the birth and boom of IT industry, the unemployment crisis has stroke all kind of workers, more and more people are facing an increasing number of challenges. At this time, an appropriate Google Google exam certification might become your biggest advantage. The specialized knowledge with Professional-Data-Engineer日本語 exam prep files are your foundation of foothold in this competitive society.

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

SectionWeightObjectives
Topic 1: Ensuring solution quality and reliability17%- Testing and validating data systems
  • 1. Data quality validation
  • 2. Performance and scalability testing
- Troubleshooting and optimization
  • 1. Diagnosing performance issues
  • 2. Optimizing queries and workloads
Topic 2: Maintaining and automating data workloads18%- Resource optimization
  • 1. Cost management and resource allocation
  • 2. Choosing appropriate compute and storage options
- Automation and repeatability
  • 1. Implementing CI/CD for data systems
  • 2. Automating deployment and updates
Topic 3: Building and operationalizing data processing systems25%- Building data pipelines
  • 1. Ingesting data from various sources
  • 2. Transforming and cleaning data
  • 3. Orchestrating data workflows
- Deploying and managing systems
  • 1. Managing infrastructure and resources
  • 2. Monitoring and logging data processes
Topic 4: 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
Topic 5: Designing data processing systems20%- Designing for regulatory and security requirements
  • 1. Ensuring data privacy and compliance
  • 2. Implementing access control and data protection
- Designing for business requirements
  • 1. Designing for reliability and fault tolerance
  • 2. Designing for scalability and elasticity
  • 3. Selecting appropriate storage solutions

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