Description

The Google Cloud Professional Cloud Database Engineer certification validates that you can design, create, manage, and troubleshoot Google Cloud databases. The exam covers four Domains: designing scalable and highly available cloud database solutions; managing a solution that spans multiple database technologies; migrating data solutions; and deploying scalable, highly available databases on Google Cloud. Expect scenarios on choosing between managed and self-managed, relational and NoSQL databases, migrating existing workloads, and supporting generative AI use cases.

Exam domains

Exam Study Guide

Determine how applications will connect to the database

Configure networking, key management, encryption, and security is how applications reach Google Cloud databases over private paths while stored data and network traffic stay protected. Virtual Private Cloud (VPC) networks carry private database traffic, Private Service Access links customer networks to Google-managed networks, and identity and perimeter controls limit who can connect and where data can move. Customer-Managed Encryption Keys (CMEK) in Cloud Key Management Service (KMS) protect data at rest, while Secure Sockets Layer (SSL) and Transport Layer Security (TLS) protect data in transit.

Continue with this study guide: Determine how applications will connect to the database

Evaluate appropriate database solutions on Google Cloud

Differentiating managed and unmanaged database services means deciding who owns patching, backups, replication, and failover. Self-managed databases leave that work to the operator, while Google-managed services automate it behind a service API.

Continue with this study guide: Evaluate appropriate database solutions on Google Cloud

Evaluate database high availability and disaster recovery options given the requirements

Evaluating tradeoffs between zonal, regional, and multi-regional database deployments is the comparison of how well each design survives zone and region failures. The comparison uses Recovery Time Objective (RTO), the time until the system runs again, and Recovery Point Objective (RPO), the amount of committed data that can be lost. In Google Cloud, RTO and RPO are defined by the scope of loss, meaning a zone outage, a region outage, or a multi-region outage. Wider distribution places copies of data and compute on different sides of those failure boundaries, which directly improves RTO and RPO.

Continue with this study guide: Evaluate database high availability and disaster recovery options given the requirements

Analyze relevant variables to perform database capacity and usage planning

Solution sizing based on current workload metrics and future requirements is the process of converting measured database demand into the right Google Cloud resources with room to grow. The work starts with a baseline of peak CPU, memory, I/O, and network use, translates that baseline into Cloud SQL, AlloyDB, Spanner, or Spanner Omni units, and then adds headroom for growth and failover. Undersizing causes slow migrations, errors, and outages, while oversizing wastes money.

Continue with this study guide: Analyze relevant variables to perform database capacity and usage planning

Exam Study Guide

Automate common database tasks

Data export and archival in Cloud SQL moves database contents into portable files in Cloud Storage for reuse, copying, and long-term retention. An export creates an external file that can recreate data, copy a single database or table, and remain available even if the source instance is deleted. Backups and point-in-time recovery instead support fast operational restore of the instance itself. Exports are therefore the choice when data must be portable, selective, or retained after cleanup.

Continue with this study guide: Automate common database tasks

Optimize database cost and performance in Google Cloud

Assessing scaling up and scaling out means deciding whether to make a single database instance larger or to add more copies across zones and regions. Scaling up adds vCPUs, memory, and storage to one instance for more compute and throughput. Scaling out adds replicas, clusters, or distributed copies to handle more reads, more connections, and failures without overloading the primary.

Continue with this study guide: Optimize database cost and performance in Google Cloud

Design database backup and recovery solutions

Geographic and automated backup planning is the practice of storing restorable copies in separate locations on a schedule so data survives zonal failure, regional failure, and human error. In Google Cloud, Cloud Storage bucket locations, managed database replication, and schedulers such as Cloud Scheduler provide the foundation. The design must balance availability against compliance, residency rules, recovery point objective (RPO), and recovery time objective (RTO).

Continue with this study guide: Design database backup and recovery solutions

Configure database monitoring and troubleshooting options

Assessing slow queries, locking, and missing indexes means tracing why reads are slow and whether contention or a missing index is the cause. Slow reads increase input/output (I/O), raise latency, and can overload a primary instance and its replicas. The work flows from latency measurement to query review to lock and storage checks, so each layer either explains the delay or points to the next layer.

Continue with this study guide: Configure database monitoring and troubleshooting options

Determine database connectivity and access management considerations

Database authentication models for AlloyDB and Cloud SQL define how a client proves its identity before it can connect. Built-in database authentication uses a username and password stored inside the database, with access controlled by GRANT and REVOKE statements. Identity and Access Management (IAM) database authentication uses Google identities such as users, groups, and service accounts, with IAM roles plus database privileges controlling access.

Continue with this study guide: Determine database connectivity and access management considerations

Exam Study Guide

Design and implement data migration and replication

Online migration with continuous replication keeps the source database online while changes flow to the Google Cloud target. The source, such as Amazon RDS or Amazon Aurora, continues to serve reads and writes while Cloud SQL or AlloyDB for PostgreSQL catches up. The migration is complete only when traffic switches to the target, replication stops, and all reads and writes happen on the target. This pattern is also called trickle migration because work moves in small chunks.

Continue with this study guide: Design and implement data migration and replication

Exam Study Guide

Apply concepts to implement scalable and highly available databases in Google Cloud.

Provisioning highly available database solutions in Google Cloud means keeping databases reachable when a zone or region fails. A zone is a separate location inside a region, and high availability spreads copies of data across zones or regions so one failure does not stop service. This design uses synchronous replication inside a region, automatic failover between zones, and readable copies across regions for wider outages.

Continue with this study guide: Apply concepts to implement scalable and highly available databases in Google Cloud.

Description

Exam domains

Exam Study Guide

Professional Cloud Database Engineer

Description

Course details

Exam domains

Determine how applications will connect to the database

Evaluate appropriate database solutions on Google Cloud

Evaluate database high availability and disaster recovery options given the requirements

Analyze relevant variables to perform database capacity and usage planning

Automate common database tasks

Optimize database cost and performance in Google Cloud

Design database backup and recovery solutions

Configure database monitoring and troubleshooting options

Determine database connectivity and access management considerations

Design and implement data migration and replication

Apply concepts to implement scalable and highly available databases in Google Cloud.

Professional Cloud Database Engineer

Description

Course details

Exam domains

Determine how applications will connect to the database

Evaluate appropriate database solutions on Google Cloud

Evaluate database high availability and disaster recovery options given the requirements

Analyze relevant variables to perform database capacity and usage planning

Automate common database tasks

Optimize database cost and performance in Google Cloud

Design database backup and recovery solutions

Configure database monitoring and troubleshooting options

Determine database connectivity and access management considerations

Design and implement data migration and replication

Apply concepts to implement scalable and highly available databases in Google Cloud.

Professional Cloud Database Engineer

Description

Course details

Exam domains

1Design innovative, scalable, and highly available cloud database solutions~32%

1.1Determine how applications will connect to the database

1.2Evaluate appropriate database solutions on Google Cloud

1.3Evaluate database high availability and disaster recovery options given the requirements

1.4Analyze relevant variables to perform database capacity and usage planning

2Manage a solution that can span multiple database technologies~25%

2.1Automate common database tasks

2.2Optimize database cost and performance in Google Cloud

2.3Design database backup and recovery solutions

2.4Configure database monitoring and troubleshooting options

2.5Determine database connectivity and access management considerations

3Migrate data solutions~23%

3.1Design and implement data migration and replication

4Deploy scalable and highly available databases in Google Cloud~20%

4.1Apply concepts to implement scalable and highly available databases in Google Cloud.

Professional Cloud Database Engineer

Description

Course details

Exam domains

1Design innovative, scalable, and highly available cloud database solutions~32%

1.1Determine how applications will connect to the database

1.2Evaluate appropriate database solutions on Google Cloud

1.3Evaluate database high availability and disaster recovery options given the requirements

1.4Analyze relevant variables to perform database capacity and usage planning

2Manage a solution that can span multiple database technologies~25%

2.1Automate common database tasks

2.2Optimize database cost and performance in Google Cloud

2.3Design database backup and recovery solutions

2.4Configure database monitoring and troubleshooting options

2.5Determine database connectivity and access management considerations

3Migrate data solutions~23%

3.1Design and implement data migration and replication

4Deploy scalable and highly available databases in Google Cloud~20%

4.1Apply concepts to implement scalable and highly available databases in Google Cloud.

Determine how applications will connect to the database

Evaluate appropriate database solutions on Google Cloud

Evaluate database high availability and disaster recovery options given the requirements

Analyze relevant variables to perform database capacity and usage planning

Automate common database tasks

Optimize database cost and performance in Google Cloud

Design database backup and recovery solutions

Configure database monitoring and troubleshooting options

Determine database connectivity and access management considerations

Design and implement data migration and replication

Apply concepts to implement scalable and highly available databases in Google Cloud.

Determine how applications will connect to the database

Evaluate appropriate database solutions on Google Cloud

Evaluate database high availability and disaster recovery options given the requirements

Analyze relevant variables to perform database capacity and usage planning

Automate common database tasks

Optimize database cost and performance in Google Cloud

Design database backup and recovery solutions

Configure database monitoring and troubleshooting options

Determine database connectivity and access management considerations

Design and implement data migration and replication

Apply concepts to implement scalable and highly available databases in Google Cloud.