Google Professional-Data-Engineer Exam : Google Certified Professional Data Engineer Exam

  • Exam Code: Professional-Data-Engineer
  • Exam Name: Google Certified Professional Data Engineer Exam
  • Updated: Jul 29, 2026
  • Q & A: 433 Questions and Answers

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About Google Professional-Data-Engineer Exam Questions

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

Who is the Professional Data Engineer Exam Intended for?

This exam is designed for individuals who are experts in designing, building, securing, and monitoring data processing systems with a particular emphasis on compliance and security. The candidate who wants to take the Professional Data Engineer exam should have the ability to deploy, leverage, and training pre-existing machine learning models. Moreover, every applicant should have experience of more than 3 years including 1-year experience in designing and handling solutions utilizing GCP.

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

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

Google Professional-Data-Engineer 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. Managing infrastructure and resources
  • 2. Monitoring and logging data processes
Ensuring solution quality and reliability17%- Testing and validating data systems
  • 1. Data quality validation
  • 2. Performance and scalability testing
- Troubleshooting and optimization
  • 1. Optimizing queries and workloads
  • 2. Diagnosing performance issues
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
Operationalizing machine learning models20%- Preparing data for ML
  • 1. Handling structured and unstructured data
  • 2. Feature engineering and data preparation
- Deploying and maintaining ML models
  • 1. Optimizing model performance and cost
  • 2. Model serving and monitoring
Designing data processing systems20%- Designing for business requirements
  • 1. Designing for scalability and elasticity
  • 2. Designing for reliability and fault tolerance
  • 3. Selecting appropriate storage solutions
- Designing for regulatory and security requirements
  • 1. Ensuring data privacy and compliance
  • 2. Implementing access control and data protection

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