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Google Cloud Professional Data Engineer Study Guide

Google Cloud Certified - Professional Data Engineer

Google Cloud Professional Data Engineer is Google Cloud's professional-level certification for engineers who design, build, secure and operate data processing systems and make data usable for analysis and AI. It covers designing data processing systems, ingesting and processing data, storing data, preparing and using data for analysis, and maintaining and automating data workloads. Google recommends 3 or more years of industry experience, including 1 or more years designing and managing solutions on Google Cloud.

  • Professional level
  • 5 exam sections
  • 40 to 50 questions

This guide is free, with no sign-up. It explains every Google Cloud Professional Data Engineer exam topic in plain English, one chapter at a time. Read them in order or jump straight to the topic you need.

  • 5chapters
  • 21topics
  • 5diagrams and exhibits
  • 1 h 46 minfast track
Start with Designing data processing systems
  • Every topic follows the same simple pattern: what it is, how it works, common mistakes
  • Diagrams and real command output where a picture explains it best
  • Exam tips and worked subnetting and IPv6 practice, with answers

How the exam is weighted

Each domain is worth this share of the exam, so spend your study time in proportion. Select a domain to open its chapter.

Chapters

Open a chapter to see its topics and how long each takes.

1Designing data processing systems5 topics · 25 min fast trackSecurity, compliance, reliability, portability and migrations.
  1. 1IAM, Encryption Keys and PII Protection5 min
  2. 2Data Sovereignty, Compliance and Environment Design5 min
  3. 3Reliability, Data Quality and ACID Choices5 min
  4. 4Flexible, Portable Designs and Data Discovery5 min
  5. 5Planning and Validating Data Migrations5 min
Read the chapter overview →
2Ingesting and processing the data4 topics · 20 min fast trackPipeline planning, Dataflow, Dataproc, streaming and CI/CD.
  1. 1Planning Data Pipelines on Google Cloud5 min
  2. 2Choosing Processing Services and Building Batch Pipelines5 min
  3. 3Streaming, Windowing, Late Data and AI Enrichment5 min
  4. 4Deploying Pipelines with Orchestration and CI/CD5 min
Read the chapter overview →
3Storing the data4 topics · 20 min fast trackChoosing storage, BigQuery warehouse design, data lakes with BigLake and Dataplex.
  1. 1Selecting Storage Systems by Access Pattern5 min
  2. 2Designing a BigQuery Data Warehouse5 min
  3. 3Managing a Data Lake with Cloud Storage and BigLake5 min
  4. 4Data Platform Design and Federated Governance5 min
Read the chapter overview →
4Preparing and using data for analysis3 topics · 15 min fast trackBI performance, masking, BigQuery ML, RAG and data sharing.
  1. 1Preparing Data for Visualization and BI5 min
  2. 2Preparing Data for ML, Embeddings and RAG5 min
  3. 3Sharing Data, Datasets and Reports5 min
Read the chapter overview →
5Maintaining and automating data workloads5 topics · 26 min fast trackCost, Composer DAGs, capacity, monitoring and failover.
  1. 1Optimizing Cost and Resources for Data Workloads5 min
  2. 2Cloud Composer DAGs and Repeatable Scheduling5 min
  3. 3BigQuery Editions, Reservations and Query Priority5 min
  4. 4Monitoring and Troubleshooting Data Processes5 min
  5. 5Fault Tolerance, Recovery and Failover6 min
Read the chapter overview →

Ready for more practice?

The full Google Cloud Professional Data Engineer question bank has 733 questions, with timed practice exams and an explanation for every answer.

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This study guide is independent and is not affiliated with, endorsed by, or sponsored by Google Cloud. Vendor names, certification names and exam codes are trademarks of their respective owners and are used for identification only.