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.

Professional Cloud Architect

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Same-Wave Migration for Latency-Coupled Tiers

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

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Explanations

...

View source documentation

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Same-Wave Migration for Latency-Coupled Tiers

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

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?

...

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.

...

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 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.

...

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.

...

Explanations

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.

...

View source documentation

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An enterprise enforces mandatory 2-Step Verification (2SV) across all user accounts in Cloud Identity. A security team needs to establish a secure recovery workflow for users who lose their physical security keys and want to proactively detect security exceptions, such as unauthorized disabling of 2SV or suspicious account activity. Which combination of administrative delegation and auditing controls should the security team implement?

Configuring and enforcing 2-step verification.

Want more questions like this?

Get a free certification question every week.

Last updated August 28, 2026

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.