Professional Data Engineer exam
40-50 multiple choice and multiple select questions.
- Type
- Written
- Delivery
- Both
- Duration
- 120 min
Exam sections
Section 1: Designing data processing systems
Design for security, compliance, reliability, and flexibility. Covers IAM, encryption, PII privacy, and regional data sovereignty. Includes data cleaning, pipeline monitoring, disaster recovery, fault tolerance, and planning migrations using BigQuery Data Transfer Service and Database Migration Service.
Preparation tips
Understand IAM hierarchy, encryption (CMEK), compliance (GDPR/HIPAA), ACID concepts, and migration tools like BigQuery Data Transfer Service and Datastream.
Section 2: Ingesting and processing the data
Plan, build, and operationalize data pipelines. Focuses on identifying sources and sinks, transformation logic, and selecting services like Dataflow, Apache Beam, Dataproc, and Pub/Sub. Includes batch and streaming transformations, AI data enrichment, and job automation using Cloud Composer.
Preparation tips
Hands-on with Dataflow/Apache Beam, Pub/Sub ingestion, Cloud Composer DAG design, and CI/CD tools like Cloud Build for pipeline promotion.
Section 3: Storing the data
Select and manage storage systems including BigQuery, Spanner, AlloyDB, and Cloud Storage. Covers data warehouse modeling, normalization, data lake management via Dataplex, and building federated governance models for distributed systems while optimizing for cost and performance.
Preparation tips
Compare storage options (latency, consistency, scalability), practice schema design in BigQuery, and review Dataplex governance and data catalog features.
Section 4: Preparing and using data for analysis
Prepare data for visualization and AI/ML applications. Includes connecting BI tools, optimizing BigQuery BI Engine, feature engineering with BigQueryML, and preparing unstructured data for embeddings and RAG. Also covers data sharing through BigQuery Analytics Hub and authorized views.
Preparation tips
Master query tuning and materialized views in BigQuery, BigQueryML workflows, and data-sharing mechanisms like Authorized Views and Analytics Hub.
Section 5: Maintaining and automating data workloads
Optimize resources, automate workloads, and monitor data processes. Focuses on cost minimization, capacity management with BigQuery Editions, and observability using Cloud Monitoring and Logging. Includes troubleshooting billing or quota issues and designing fault-tolerant systems with failover.
Preparation tips
Familiarize with cost-control features like BigQuery Reservations, building Composer DAGs, and using Cloud Monitoring dashboards for diagnosing failures.