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 01, 2026
  • Q&As: 433 Questions and Answers

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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.

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

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

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

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Introduction

Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages They collect, transform, and visualize data. The Data Engineer designs, builds, maintains, and troubleshoots data processing systems with a particular emphasis on the security, reliability, fault-tolerance,scalability, fidelity, and efficiency of such systems.

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

SectionWeightObjectives
Building and operationalizing data processing systems25%- Building data pipelines
  • 1. Ingesting data from various sources
  • 2. Orchestrating data workflows
  • 3. Transforming and cleaning data
- Deploying and managing systems
  • 1. Monitoring and logging data processes
  • 2. Managing infrastructure and resources
Maintaining and automating data workloads18%- Automation and repeatability
  • 1. Automating deployment and updates
  • 2. Implementing CI/CD for data systems
- Resource optimization
  • 1. Cost management and resource allocation
  • 2. Choosing appropriate compute and storage options
Ensuring solution quality and reliability17%- Testing and validating data systems
  • 1. Performance and scalability testing
  • 2. Data quality validation
- Troubleshooting and optimization
  • 1. Optimizing queries and workloads
  • 2. Diagnosing performance issues
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. Optimizing model performance and cost
  • 2. Model serving and monitoring
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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