An enterprise is planning the migration of a mission-critical three-tier enterprise workload from an on-premises data center to Google Cloud. Architectural dependency mapping reveals the following characteristics: Application Tier: Multiple microservices and web instances with strict sub-5 ms latency requirements to the relational database. Database Tier: High-transaction database requiring 24/7 availability with minimal scheduled downtime during cutover. Downstream Analytics: Scheduled extract, transform, and load (ETL) and reporting pipelines that read from the transactional database. Preliminary testing shows that splitting the application tier in Google Cloud from an on-premises database introduces unacceptable cross-premises network latency. Which migration sequencing and data synchronization strategy should the cloud architect recommend?

Group the application tier and database into the same migration wave, establish continuous Change Data Capture (CDC) replication to Google Cloud, and cut over both tiers together once replication lag reaches near zero.: ### What This Strategy Is This strategy employs **dependency-aware migration wave planning** combined with continuous data replication using **Change Data Capture (CDC)** to migrate tightly coupled tiers without causing cross-premises latency penalties or extended downtime. ### How It Meets the Requirements - **Latency Requirements**: Because the application tier requires sub-5 ms response times to the database, co-locating the compute instances and the target database within the same Google Cloud Virtual Private Cloud (VPC) in a single migration wave prevents cross-premises network latency across the hybrid connection. - **Operational Continuity & Minimal Downtime**: Setting up continuous **CDC replication** (via tools such as Database Migration Service) allows the database to copy the initial dataset and continuously stream ongoing transactions while the on-premises system remains live. Cutover requires only a brief maintenance window to finalize remaining transactions and point client traffic to Google Cloud. - **Dependency Alignment**: Grouping tightly coupled dependencies into the same wave avoids distributed "chatty" network calls over long-distance links. ### Key Advantages - **Eliminates Latency Degradation**: Tightly coupled components move together, maintaining fast intra-VPC communication. - **Near-Zero Data Lag**: Continuous CDC ensures target database synchronization before cutover occurs. - **Predictable Cutover**: Validation and testing can occur on Google Cloud staging instances prior to promoting the database. - **Downstream Decoupling**: Downstream reporting and analytics can be orchestrated in subsequent migration waves once the primary system is stable in Google Cloud. ### Why This Is Optimal Decoupling components that have hard sub-5 ms latency dependencies across a hybrid WAN link inevitably leads to application performance degradation. By keeping dependent tiers within the same migration wave and leveraging continuous CDC data streaming, the architecture minimizes both performance risk and cutover downtime.

StudyCerts

Migration Sequencing Dependency Mapping Database Cdc | Intrigued by the art of cloud architecture? Discover how to design, develop, and manage robust, secure, scalable, and dynamic solutions on Google Cloud as you prepare for the Professional Cloud Architect exam! | StudyCerts

Professional Cloud Architect

Intrigued by the art of cloud architecture? Discover how to design, develop, and manage robust, secure, scalable, and dynamic solutions on Google Cloud as you prepare for the Professional Cloud Architect exam!

Intermediate

Prepare and test your skills

Track Readiness

Set Exam Goal

Intermediate

Prepare and test your skills

Track Readiness

Set Exam Goal

Migration Sequencing Dependency Mapping Database Cdc

Section: Using migration methodologies, workload testing, network planning, and dependency planning

Worked example. The correct answer is already marked and every option is explained below, so there is nothing to select here. To answer questions yourself, start the free trial.

An enterprise is planning the migration of a mission-critical three-tier enterprise workload from an on-premises data center to Google Cloud. Architectural dependency mapping reveals the following characteristics:

Application Tier: Multiple microservices and web instances with strict sub-5 ms latency requirements to the relational database.
Database Tier: High-transaction database requiring 24/7 availability with minimal scheduled downtime during cutover.
Downstream Analytics: Scheduled extract, transform, and load (ETL) and reporting pipelines that read from the transactional database.

Preliminary testing shows that splitting the application tier in Google Cloud from an on-premises database introduces unacceptable cross-premises network latency.

Which migration sequencing and data synchronization strategy should the cloud architect recommend?

Group the application tier and database into the same migration wave, establish continuous Change Data Capture (CDC) replication to Google Cloud, and cut over both tiers together once replication lag reaches near zero.

Migrate the downstream analytics and reporting pipelines in Wave 1, point them to write back to the on-premises database, and take offline cold backups during full cutover.

Migrate the database to Google Cloud in Wave 1 using scheduled maintenance exports, while leaving the application tier on-premises connected across public internet endpoints.

Migrate the application tier to Compute Engine in Wave 1 while keeping the database on-premises, using scheduled CSV/JSON flat file batch exports to backfill transactions.

Professional Cloud Architect

Migration Sequencing Dependency Mapping Database Cdc

Related questions