Google Certified Professional Data Engineer : Professional-Data-Engineer exam

Google Professional-Data-Engineer Actual PDF
  • Exam Code: Professional-Data-Engineer
  • Exam Name: Google Certified Professional Data Engineer Exam
  • Updated: Aug 23, 2026
  • Q & A: 433 Questions and Answers
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About Google Certified Professional Data Engineer : Professional-Data-Engineer Exam

The candidates must develop practical skills in the exam topics to succeed. These objectives are highlighted below:

Design Data Processing Systems

  • Design Data Processing Solutions: This topic includes the individuals’ expertise in planning, distributed systems usage, choice of infrastructure, hybrid Cloud & edge computing, system availability & fault tolerance. You should also know about the architecture options, including message queues, message brokers, service-oriented architecture, middleware, and serverless function;
  • Migrate Data Processing & Data Warehousing: This section includes validating migrations, migration from on-premises to Cloud, and awareness of the current state & how to migrate designs to the future state.
  • Select the Relevant Storage Technologies: The considerations for this area include mapping storage systems to the business needs, data modeling, distributed systems, as well as tradeoffs, involving transactions, throughput, and latency;
  • Design Data Pipeline: The focus for this subsection includes data visualization & publishing and batch & streaming data (Cloud Dataproc, Cloud Dataflow, Cloud Sub/Pub, Hadoop ecosystem, Apache Spark, Apache Beam, and Apache Kafka). It also focuses on online versus batch prediction and job orchestration & automation;

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

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Preparation Process

To perform well in the Google Professional Data Engineer certification exam, the candidates must be ready to devote ample time to preparation. There is a host of study materials available on the Internet, but if you want to be confident in the authenticity of the resources that you use, it is best to refer to the official platform. Google recommends that the applicants follow the Professional Data Engineer learning path, which is a comprehensive option involving in-person classes, online training, hands-on labs, and other resources from Google Cloud.

Besides that, it is recommended that the students use the official sample questions to familiarize themselves with the question formats that they will encounter during the actual exam. The official webpage also contains additional resources such as Google Cloud documentation and Google Cloud solutions. There is also an option of joining the subject-related webinar to get valuable preparation tips from the Google experts.

Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Designing data processing systems20%- Designing for regulatory and security requirements
  • 1. Implementing access control and data protection
  • 2. Ensuring data privacy and compliance
- Designing for business requirements
  • 1. Designing for scalability and elasticity
  • 2. Designing for reliability and fault tolerance
  • 3. Selecting appropriate storage solutions
Topic 2: 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
Topic 3: Maintaining and automating data workloads18%- Resource optimization
  • 1. Choosing appropriate compute and storage options
  • 2. Cost management and resource allocation
- Automation and repeatability
  • 1. Automating deployment and updates
  • 2. Implementing CI/CD for data systems
Topic 4: Building and operationalizing data processing systems25%- Building data pipelines
  • 1. Orchestrating data workflows
  • 2. Ingesting data from various sources
  • 3. Transforming and cleaning data
- Deploying and managing systems
  • 1. Managing infrastructure and resources
  • 2. Monitoring and logging data processes
Topic 5: Ensuring solution quality and reliability17%- Troubleshooting and optimization
  • 1. Diagnosing performance issues
  • 2. Optimizing queries and workloads
- Testing and validating data systems
  • 1. Data quality validation
  • 2. Performance and scalability testing

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