[NEW] Google Cloud Professional Cloud Database Engineer

Detailed Exam Domain CoverageDesign innovative, scalable, and highly available cloud database solutions (32%)Analyze relevant variables to perform database capacity and usage planning.Evaluate performance and cost trade‑offs of different database configurations.Determine how applications will connect to the database.Deploy scalable and highly available databases in Google Cloud (32%)Apply concepts to implement scalable and highly available databases in Google Cloud.Provision highly available database solutions in Google Cloud.Test high availability and disaster recovery strategies.Manage a solution that can span multiple database solutions (20%)Evaluate trade‑offs between multi‑regional, regional, and zonal database deployment strategies.Define maintenance windows and notifications based on application availability requirements.Assess auditing policies for managed services.Migrate data solutions (16%)Evaluate appropriate database solutions on Google Cloud.Differentiate between managed and unmanaged database services.Analyze the cost of running database solutions in Google Cloud.Course DescriptionPassing the Google Cloud Professional Cloud Database Engineer certification requires more than just memorizing documentation. It demands a deep, practical understanding of how to architect, migrate, and manage robust database solutions across the entire Google Cloud ecosystem. I designed these practice tests to mirror the complexity, format, and domain weighting of the actual exam so you can step into your testing session with absolute confidence.Whether you are evaluating the nuances between Cloud Spanner and Cloud SQL, planning a zero-downtime migration, or determining the most cost-effective disaster recovery strategy, these questions will test your limits. I have carefully crafted every scenario to challenge your troubleshooting and architectural design skills. Instead of just telling you which answer is correct, I break down the technical reasoning behind every single option, ensuring you understand exactly why a specific configuration works and why the alternatives fall short.By working through this comprehensive question bank, you will identify your knowledge gaps, reinforce your understanding of multi-regional deployments, and learn how to translate complex business requirements into scalable Google Cloud database architectures.Practice Questions PreviewQuestion 1: You are planning to migrate an on-premises MySQL database to Google Cloud. The application requires strict relational consistency, high availability (HA) across multiple zones to survive a zone failure, and automated failover. The database size is roughly 2 TB. Which solution should you implement?Options:A. Cloud SQL for MySQL with Regional High Availability (HA) enabled.B. Cloud Spanner configured for a single regional deployment.C. Compute Engine instances running MySQL with asynchronous replication.D. Cloud SQL for MySQL in a single zone with multiple read replicas.E. Bare Metal Solution running Oracle.F. Cloud Bigtable with a multi-cluster routing profile.Correct Answer: A. Cloud SQL for MySQL with Regional High Availability (HA) enabled.Detailed Explanation:Option A is correct: Cloud SQL with Regional HA creates a primary instance and a standby instance in a different zone within the same region. It uses synchronous replication and provides automated failover, perfectly matching the 2 TB size and MySQL engine requirement.Option B is incorrect: While Spanner offers HA and relational consistency, migrating a standard 2 TB MySQL database directly to Spanner requires significant schema and application code changes. Cloud SQL is the direct, appropriate path for a lift-and-shift MySQL migration of this size.Option C is incorrect: Running unmanaged MySQL on Compute Engine introduces heavy operational overhead. You would have to manually configure, monitor, and manage the HA and failover mechanisms, which defeats the purpose of utilizing Google Cloud's managed services.Option D is incorrect: Read replicas provide horizontal scaling for read queries but do not provide automated failover for high availability in the event of a zone failure.Option E is incorrect: Bare Metal Solution is designed specifically for specialized, legacy workloads like Oracle databases that cannot easily be modernized or virtualized. It is entirely unnecessary for a standard MySQL workload.Option F is incorrect: Cloud Bigtable is a NoSQL wide-column store. It does not support relational consistency or SQL queries, making it fundamentally incompatible with a MySQL database migration.Question 2: Your IoT application generates millions of events per second. You need a database capable of handling massive, high-throughput write operations with single-digit millisecond latency. The data is time-series in nature and structured as wide columns. Which Google Cloud database is the best fit?Options:A. Cloud BigtableB. Firestore in Native ModeC. Cloud SpannerD. Cloud SQL for PostgreSQLE. Firestore in Datastore ModeF. BigQueryCorrect Answer: A. Cloud BigtableDetailed Explanation:Option A is correct: Cloud Bigtable is a fully managed, scalable NoSQL wide-column store specifically designed for massive scale, single-digit millisecond latency, and extremely high write throughput (like IoT and time-series data).Option B is incorrect: Firestore in Native Mode is an excellent NoSQL document database for web and mobile apps offering real-time synchronization, but it is not optimized for millions of writes per second or time-series data at the scale of IoT workloads.Option C is incorrect: Cloud Spanner is a strongly consistent, globally distributed relational database. While highly scalable, it is designed for relational data and transactions, not as a specialized time-series or wide-column store.Option D is incorrect: Cloud SQL for PostgreSQL is a traditional relational database. It will quickly become a bottleneck and fail to support millions of write operations per second without severe scaling issues.Option E is incorrect: Firestore in Datastore Mode is highly scalable for key-value and NoSQL document data but does not offer the wide-column structure or the sheer write-throughput optimization required for heavy IoT time-series ingestion.Option F is incorrect: BigQuery is an enterprise data warehouse designed for complex analytical queries (OLAP) on large datasets. It is not an operational database (OLTP) and cannot serve single-digit millisecond latency reads/writes for application ingestion.Question 3: You are evaluating the performance of a newly deployed Cloud SQL for PostgreSQL database. Monitoring alerts show that the primary instance's CPU utilization frequently hits 95% during business hours due to heavy application read traffic. Write traffic remains minimal and constant. What is the most cost-effective way to stabilize performance?Options:A. Create a read replica and route the application's read traffic to it.B. Upgrade the primary instance to a higher tier with double the vCPUs.C. Migrate the database to Cloud Spanner for horizontal write scaling.D. Enable High Availability (HA) to distribute the load across multiple zones.E. Move the database to Compute Engine to apply custom OS-level caching.F. Change the instance storage type from SSD to Standard HDD to offset costs while upgrading CPU.Correct Answer: A. Create a read replica and route the application's read traffic to it.Detailed Explanation:Option A is correct: Because the CPU spike is caused explicitly by read traffic, offloading those read queries to a read replica is the standard, most cost-effective architectural pattern. This instantly reduces the load on the primary instance.Option B is incorrect: Scaling up (increasing vCPUs on the primary) will solve the problem temporarily, but it is generally more expensive than adding a read replica and does not isolate analytical/read workloads from operational writes.Option C is incorrect: Migrating to Cloud Spanner is a massive, complex undertaking. Since the issue is just read-heavy traffic on a PostgreSQL instance, moving to Spanner is complete overkill and highly cost-inefficient.Option D is incorrect: Enabling High Availability (HA) in Cloud SQL provides an active-passive configuration for disaster recovery. The standby instance cannot be used to serve read traffic, so this would not solve the CPU utilization issue.Option E is incorrect: Moving to an unmanaged Compute Engine instance increases administrative burden dramatically and is contrary to cloud-native best practices. Managed Cloud SQL already provides better scalability options.Option F is incorrect: Changing SSD to HDD will drastically reduce IOPS and overall database performance, likely causing massive latency bottlenecks. It is terrible practice for an active operational database.What You Get With This Course:Welcome to the Mock Exam Practice Tests Academy to help you prepare for your Google Cloud Professional Cloud Database Engineer certification.You can retake the exams as many times as you want.This is a huge original question bank.You get support from instructors if you have questions.Each question has a detailed explanation.Mobile-compatible with the Udemy app.I hope that by now you're convinced! And there are a lot more questions inside the course.