GCP Professional Data Engineer
A structured study guide covering all seven exam domains, with practice questions and 35 hands-on labs modeled on real exam difficulty.
Start free: all 7 study guides and 3 full hands-on labs are open now — no account needed for the first lab. Pick a lab →
About the Exam
The Google Cloud Professional Data Engineer certification validates your ability to design, build, secure, and optimize data processing systems on GCP. The exam is 2 hours, ~50-60 questions, and tests practical architecture decisions rather than trivia recall.
Study Domains
1. Data Ingestion
Pub/Sub, Dataflow, Cloud Data Fusion, Transfer Service, Datastream, and patterns for batch vs. streaming ingestion.
Start studying →2. Data Processing
Dataflow (Apache Beam), Dataproc (Spark/Hadoop), BigQuery SQL, windowing, triggers, and pipeline orchestration with Cloud Composer.
Start studying →3. Data Storage
GCS storage classes and lifecycle, Cloud Bigtable row key design, Cloud Spanner global consistency, Cloud SQL, and Firestore.
Start studying →4. Analytics
BigQuery partitioning, clustering, materialized views, INFORMATION_SCHEMA, BI Engine, Looker, and cost optimization strategies.
Start studying →5. Operations
Cloud Composer orchestration, Dataflow Flex Templates, Cloud Monitoring and Logging, cost attribution, and operational best practices.
Start studying →6. Machine Learning
Vertex AI, AutoML, Feature Store, ML pipelines, model serving, drift monitoring, and MLOps best practices.
Start studying →7. Security & Compliance
IAM, VPC Service Controls, Cloud DLP, CMEK encryption, data governance, Dataplex lineage, and regulatory compliance patterns.
Start studying →Interactive Quiz
Scenario-based questions across all 7 exam domains. Track your score and progress.
Take the quiz →Hands-On Labs
35 practical labs covering all 7 domains. Each includes local validation steps and a Jupyter Notebook to run against a real GCP project.
Browse labs →Key Services at a Glance
Click any service to expand a study card — overview, key features, when to use, and exam tips.
Suggested Study Plan
Week 1 — Data Ingestion
Focus on Pub/Sub messaging guarantees, Dataflow's unified batch/stream model, Datastream CDC, Storage Transfer Service, and patterns for choosing between ingestion tools based on latency and cost requirements.
Week 2 — Data Processing
Deep-dive into Apache Beam concepts (windowing, triggers, watermarks), Dataproc cluster tuning, ephemeral vs. persistent clusters, and Cloud Composer DAG design patterns including ExternalTaskSensor.
Week 3 — Data Storage
Cover GCS lifecycle policies, Bigtable row key design and hotspot avoidance, Cloud Spanner global transactions and interleaving, Cloud SQL HA and PITR, and Firestore Native vs. Datastore mode.
Week 4 — Analytics
Master BigQuery partitioning and clustering, materialized views, INFORMATION_SCHEMA queries for cost analysis, on-demand vs. flat-rate pricing, BI Engine, and Looker vs. Looker Studio selection.
Week 5 — Operations
Study Dataflow Flex Templates and operational metrics (system lag, data freshness), Cloud Composer environment tuning, cost attribution with labels, and Cloud Logging log sinks and audit logs.
Week 6 — Machine Learning
Cover Vertex AI end-to-end (training, tuning, deployment), Feature Store freshness, BigQuery ML (ARIMA_PLUS, AutoML), model monitoring and drift detection, Document AI, and distributed training patterns.
Week 7 — Security, Compliance & Review
Study IAM best practices, VPC Service Controls perimeter design, Cloud DLP tokenization and de-identification, CMEK, Dataplex data lineage, and GDPR crypto-shredding. Spend the final 2-3 days on full practice exams.