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 19, 2026
  • Q & A: 433 Questions and Answers

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

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

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

Data Engineering on Google Cloud course

It is a 4-day course that gives hands-on experience to the candidates and allows them to build data processing systems on Google Cloud. It will also show you how to design data processing systems, analyze data and build end-to-end data pipelines and machine learning. In order to get a better understanding of the course, you need to complete the big data machine learning course or get equivalent experience. This course also aids you in developing applications using a programming language such as Python and covers the following objective:

  • Predicting machine models using TensorFlow and Cloud ML
  • Designing and building data processing systems on the Google Cloud Platform
  • Processing batch and streaming data by using autoscaling data pipelines on Cloud Dataflow
  • Enable insights from streaming data
  • Influencing unstructured data using ML APIs on Cloud Dataproc

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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.

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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Designing data processing systems20%- Designing for business requirements
  • 1. Designing for reliability and fault tolerance
  • 2. Designing for scalability and elasticity
  • 3. Selecting appropriate storage solutions
- Designing for regulatory and security requirements
  • 1. Implementing access control and data protection
  • 2. Ensuring data privacy and compliance
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%- 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
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
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

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