Professional Data Engineer: pipelines and data decisions

Objectives and progression

Eight lessons with guided application, 50 questions, and eight original cases. Internal assessment of 32 decisions in 60 minutes. Fictional banking examples cover quality, migration, streaming, sharing, costs, and recovery. Initial coverage without executable labs or exhaustive treatment of every storage product or advanced objective. Current Google-linked standard guide, document title v4.2, inspected 2026-09-30; edition date unconfirmed. Five approximate weights: 22/25/20/15/18. Exam of 40–50 items in 120 minutes, in English and Japanese. A public numerical passing threshold is unconfirmed.

Audience: Data and platform engineers and APS professionals designing and operating Google Cloud pipelines.

Prerequisites: SQL, pipeline, IAM, cloud, and data-management experience. No formal certification prerequisite; practical Google Cloud experience recommended.

370 estimated study minutes

• Translate data requirements into controls, ownership, and observable outcomes.
• Build quality gates and validate final state before cutover.
• Distinguish event time, window updates, and execution guarantees.
• Choose write contracts and promote transformations with validation.
• Choose data organization from queries, transactions, and retention.
• Evaluate effects of storage and query-control changes.
• Deliver useful data while preserving access scope and evaluation validity.
• Connect orchestration, diagnosis, and recovery to business deadlines.

Modules

1. Design, governance, and identity
2. Quality, migration, and cutover
3. Streaming, time, and effects
4. Ingestion, publication, and CI/CD
5. Storage and access patterns
6. Costs, expiry, and policies
7. Analysis, sharing, and ML evaluation
8. Operations, capacity, and recovery

Continue learning

Google Cloud

• Professional Cloud DevOps Engineer
• Professional Cloud Architect
• Professional Cloud Security Engineer
• SQL
• Production Support L3

References and version

Current linked standard guide (document title v4.2); edition date unconfirmed (2026-09-30 inspection)

• Professional Data Engineer certification · 2026-09-30
• Professional Data Engineer standard exam guide, document title v4.2 · 2026-09-30
• Test data with Dataform assertions · 2026-09-30
• Dataform dependencies and dependent assertions · 2026-09-30
• Dataflow exactly-once processing · 2026-09-30
• Troubleshoot Dataflow bottlenecks · 2026-09-30
• Apache Beam programming guide · 2026-09-30
• BigQuery Storage Write API gRPC · 2026-09-30
• Datastream events and streams · 2026-09-30
• BigQuery partitioned tables · 2026-09-30
• Manage partitioned tables · 2026-09-30
• BigQuery clustered tables · 2026-09-30
• Control BigQuery costs · 2026-09-30
• BigQuery row-level security · 2026-09-30
• BigQuery column-level security · 2026-09-30
• BigQuery authorized views · 2026-09-30
• BigQuery time travel and fail-safe · 2026-09-30
• BigQuery materialized views · 2026-09-30
• BigQuery workload management · 2026-09-30
• Knowledge Catalog introduction · 2026-09-30
• Writing DAGs for Managed Service for Apache Airflow · 2026-09-30
• Machine learning training and test datasets · 2026-09-30
• BigQuery embeddings and vector search · 2026-09-30
• Cloud Storage object lifecycle management · 2026-09-30
• IAM overview · 2026-09-30
• Cloud KMS key rotation · 2026-09-30
• Sensitive Data Protection pseudonymization · 2026-09-30
• Well-Architected Framework · 2026-09-30