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: Jun 17, 2026
  • Q & A: 403 Questions and Answers

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About Google Professional-Data-Engineer Exam Questions

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Google Professional-Data-Engineer Exam Syllabus Topics:

TopicDetails
Topic 1
  • Ingesting and processing the data: The topic discusses planning of the data pipelines, building the pipelines, acquisition and import of data, and deploying and operationalizing the pipelines.
Topic 2
  • Maintaining and automating data workloads: It discusses optimizing resources, automation and repeatability design, and organization of workloads as per business requirements. Lastly, the topic explains monitoring and troubleshooting processes and maintaining awareness of failures.
Topic 3
  • Preparing and using data for analysis: Questions about data for visualization, data sharing, and assessment of data may appear.
Topic 4
  • Storing the data: This topic explains how to select storage systems and how to plan using a data warehouse. Additionally, it discusses how to design for a data mesh.
Topic 5
  • Designing data processing systems: It delves into designing for security and compliance, reliability and fidelity, flexibility and portability, and data migrations.

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

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Understanding functional and technical aspects of Google Professional Data Engineer Exam Designing data processing systems

The following will be discussed here:

  • Mapping storage systems to business requirements
  • Distributed systems
  • Designing data pipelines
  • Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
  • Designing data processing systems
  • Online (interactive) vs. batch predictions
  • Data modeling
  • Tradeoffs involving latency, throughput, transactions
  • Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
  • Selecting the appropriate storage technologies
  • Job automation and orchestration (e.g., Cloud Composer)
  • Choice of infrastructure
  • Schema design
  • Use of distributed systems
  • Capacity planning
  • Data publishing and visualization (e.g., BigQuery)
  • At least once, in-order, and exactly once, etc., event processing
  • Hybrid cloud and edge computing
  • System availability and fault tolerance

Understanding functional and technical aspects of Google Professional Data Engineer Exam Operationalizing machine learning models

The following will be discussed here:

  • Hardware accelerators (e.g., GPU, TPU)
  • Continuous evaluation
  • Deploying an ML pipeline
  • Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
  • Choosing the appropriate training and serving infrastructure
  • Conversational experiences (e.g., Dialogflow)
  • ML APIs (e.g., Vision API, Speech API)
  • Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
  • Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
  • Use of edge compute
  • Ingesting appropriate data
  • Leveraging pre-built ML models as a service
  • Impact of dependencies of machine learning models
  • Measuring, monitoring, and troubleshooting machine learning models
  • Distributed vs. single machine
  • Common sources of error (e.g., assumptions about data)
  • Operationalizing machine learning models

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