Google Cloud Professional Cloud Database Engineer
Validates ability to design, create, manage, and troubleshoot Google Cloud databases. Covers designing scalable and highly available cloud database solutions including capacity planning, disaster recovery, connectivity, and database evaluation; managing solutions spanning multiple database technologies with access management, monitoring, backup/recovery, cost optimization, and automation; migrating data solutions with migration strategy design and implementation; and deploying scalable and highly available databases with provisioning, replication, and failover. 50-60 multiple-choice and multiple-select questions in 2 hours. Recommended 5+ years database experience and 2+ years with Google Cloud; 2-year validity.
Exam domains
- Design innovative, scalable, and highly available cloud database solutions32%
Designing relational databases (Cloud SQL - MySQL/PostgreSQL/SQL Server; AlloyDB - PostgreSQL-compatible; Cloud Spanner - global relational with strong consistency; choosing instance tier - shared core, dedicated core, optimized memory, optimized for analytics; high availability and disaster recovery design - regional/multi-region, read replicas, cross-region replicas, failover replicas; backups - automated daily, on-demand; encryption - default Google-managed, CMEK with Cloud KMS, CSEK; networking - private IP only, authorized networks, Cloud SQL Auth Proxy, Private Service Connect for AlloyDB and Cloud SQL). Designing non-relational databases (Firestore - document-oriented, native mode vs Datastore mode; Bigtable - wide-column for IoT/time series/large analytical workloads; Memorystore - Redis and Memcached for in-memory caching; choice between document, wide-column, key-value, time-series, graph - integrating non-Google products like Neo4j on Compute Engine). Designing data warehouses (BigQuery - serverless analytical warehouse - choice of partition and clustering, materialized views, BI Engine, slot reservations; BigQuery Omni for multi-cloud; comparing BigQuery to OLTP options). Designing for sustainability, scalability, and HA (capacity planning; horizontal scaling for read replicas; sharding strategies; multi-region failover; read replicas in different regions; HA with regional Cloud SQL instances; Spanner regional vs multi-regional; BigQuery cross-region datasets).
- Manage a solution that can span multiple database technologies25%
Choosing the right database based on use case requirements (transactional vs analytical; structured vs semi-structured vs unstructured; consistency model - strong, eventual; latency and throughput; data volume - GB to PB scale; geographical distribution; cost). Operating multiple databases (BigQuery federated queries to Cloud SQL, Spanner, Cloud Storage, Bigtable, AlloyDB; Datastream for CDC; Database Center for fleet observability; managing fleet-wide patches, OS upgrades, version upgrades for Cloud SQL). Securing databases (IAM roles - database administrator, database editor, database viewer, database client; column-level security in BigQuery; row-level security in BigQuery; dynamic data masking; database authentication - IAM database authentication, Cloud SQL IAM; encryption at rest, in transit, in use - Confidential Computing; secret management - Secret Manager for connection strings; VPC Service Controls for data exfiltration prevention).
Sources
Questions are grounded in 50 references from official and authoritative materials.