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

Exam Code: Professional-Data-Engineer

Exam Name: Google Certified Professional Data Engineer Exam

Updated: Aug 16, 2026

Q & A: 433 Questions and Answers

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About Google Professional-Data-Engineer Exam

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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.

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Career Path

Completing the exam associated with the Google Professional Data Engineer certification provides you with a great validation of your skills in designing, building, operationalizing, securing, and monitoring data processing systems. The job roles that you can take up after getting certified include a Google Cloud Data Engineer, an Operations Engineer, a Cloud Infrastructure Engineer, a DevOps Infrastructure Engineer, a Cloud Database Engineer, a Google Cloud IAM Engineer, a DataOps Engineer, a Big Data Engineer, a Google Cloud Platform Data Architect, and more. The average salary that you can expect to earn with this certificate is around $125,550 per year. However, the real remuneration will depend on a specific job title, location of an individual, and his/her working experience.

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

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Google Professional-Data-Engineer 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
Designing data processing systems20%- Designing for business requirements
  • 1. Selecting appropriate storage solutions
  • 2. Designing for scalability and elasticity
  • 3. Designing for reliability and fault tolerance
- Designing for regulatory and security requirements
  • 1. Ensuring data privacy and compliance
  • 2. Implementing access control and data protection
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. Model serving and monitoring
  • 2. Optimizing model performance and cost
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

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