Google Cloud Professional Data EngineerStudy guide
The professional certification for designing and building robust data infrastructure to collect, transform, store, and serve data on Google Cloud (Professional Data Engineer).
About Google Cloud Professional Data Engineer (GCP-PDE)
Google Cloud Professional Data Engineer (GCP-PDE) is a Professional / Expert-level certification from Google Cloud. This page organizes the exam scope into a 5-chapter, 10-section study guide and lets you check your understanding with exam-style practice questions. A good flow is to read the chapters below in order, then test yourself via "Practice questions."
Exam domains (approximate weighting)
- Designing data processing systems~22%
- Ingesting and processing the data~25%
- Storing the data~20%
- Preparing and using data for analysis~15%
- Maintaining and automating data workloads~18%
Weights are approximate guidance for the live exam. Each domain is covered in detail in the chapters and sections below.
Official exam information: https://cloud.google.com/learn/certification/data-engineer
1Designing data processing systems
- 1.1Designing for security and compliance
Understand IAM and organization policies for data platforms, encryption at rest/in transit and key management, handling PII, data residency (sovereignty), governance via project/dataset/table architecture, and multi-environment (dev/prod) design.
- 1.2Designing for reliability, migration, and data methods
Understand data preparation/cleaning (Dataform, Dataflow, Cloud Data Fusion), pipeline monitoring/orchestration, disaster recovery and fault tolerance, ACID vs availability decisions, data validation, and choosing migration tools (BigQuery Data Transfer Service, Database Migration Service, Datastream, Transfer Appliance).
2Ingesting and processing the data
- 2.1Planning and building pipelines
Understand defining data sources and sinks, transformation/orchestration logic, batch and streaming (windowing, late data) processing, and choosing the right processing services (Dataflow, Apache Beam, Dataproc, Cloud Data Fusion, BigQuery, Pub/Sub, Spark, Kafka).
- 2.2Deploying and operationalizing pipelines
Understand job automation and orchestration (Cloud Composer, Workflows), data cleansing and AI data enrichment, integrating new data sources, and continuous delivery of pipelines via CI/CD.
3Storing the data
- 3.1Choosing storage systems and the data warehouse
Understand choosing managed services by access pattern (BigQuery, BigLake, AlloyDB, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore), storage cost/performance, lifecycle management, and data-warehouse modeling (degree of normalization, optimizing for access patterns).
- 3.2Data lakes and data platforms
Understand managing data lakes (discovery, access, cost controls), building data platforms with Dataplex and Dataplex Catalog, federated governance for distributed data systems, and choosing between lakes and warehouses.
4Preparing and using data for analysis
- 4.1Preparing data for visualization
Understand connecting BI tools, speeding up with precomputation (materialized views, BI Engine), troubleshooting slow queries, and protecting analytical data with security, data masking, IAM, and Cloud DLP.
- 4.2Preparing data for AI/ML and sharing
Understand data prep for feature engineering and training/serving (BigQuery ML), preparing unstructured data for embeddings and retrieval-augmented generation (RAG), and sharing data (rules, publishing datasets, Analytics Hub).
5Maintaining and automating data workloads
- 5.1Optimizing resources and automation
Understand minimizing cost while ensuring resources for business-critical processes, deciding persistent vs job-based Dataproc clusters, automating with Cloud Composer DAGs and repeatable scheduling, and capacity management with BigQuery Editions and reservations.
- 5.2Monitoring, troubleshooting, and fault tolerance
Understand observability of data processes (Cloud Monitoring, Cloud Logging, BigQuery admin panel), troubleshooting errors/billing/quotas, and fault tolerance (running in multiple regions/zones, preparing for corruption/missing data, replication and failover).