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

Professional-Data-Engineer
  • Exam Code: Professional-Data-Engineer
  • Exam Name: Google Certified Professional Data Engineer Exam
  • Updated: Aug 22, 2026
  • Q & A: 433 Questions and Answers

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About Google Professional-Data-Engineer Exam

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Exam Details

The Google Professional Data Engineer certification exam has the duration of 2 hours. The qualifying test is made up of multiple-select and multiple-choice questions. The exam is available either in Japanese or English. To register for it, you are required to go through the official webpage and pay the fee of $200 plus applicable taxes. While completing the registration process, the potential individuals can choose the preferred method of exam delivery. It can be taken in person at the nearest testing center or online from a remote location.

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.

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.

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

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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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