An enterprise is planning the phased migration of several multi-tier enterprise applications from an on-premises VMware data center to Google Cloud. A comprehensive dependency assessment reveals the following constraints: Application Dependencies: Presentation and application tiers communicate heavily with backend database servers and require minimal inter-tier latency. Maintenance Windows: Specific business units permit application switchovers only during pre-approved, scheduled maintenance intervals. Operational Overhead: Several virtual machines have accumulated pending operating system updates that will require a host reboot. How should the cloud architect organize the migration waves and execution strategy to minimize downtime and operational disruption?

Group interdependent tiers into common migration waves based on dependency mappings and maintenance schedules, aligning pending OS update reboots with the migration switchover: What Migration Wave Planning Entails Migration wave planning is the systematic grouping and sequencing of workloads during a cloud migration. Rather than moving systems at random or all at once, architects evaluate application dependency mappings, technical constraints, and business operational windows to bundle workloads into logical, risk-managed batches. How It Meets the Requirements Application Dependencies: Grouping tightly coupled tiers (such as web, application, and database servers) within the same migration wave prevents high-latency cross-environment traffic (latency between on-premises and Google Cloud) and avoids network bottlenecks across hybrid interconnects. Maintenance Windows: Aligning each wave with existing business unit maintenance schedules ensures switchovers occur during agreed periods of low transactional activity. Minimizing Operational Overhead: Aligning pending operating system updates with the scheduled switchover reboot consolidates two necessary power cycles into a single maintenance event, reducing system downtime. Key Advantages and Capabilities Reduced Inter-Tier Latency: Prevents degradation of multi-tier applications by migrating interdependent services simultaneously. Minimized Business Disruption: Adheres to enterprise change management windows and Service Level Objectives (SLOs). Efficient Resource Utilization: Streamlines administrative overhead by avoiding redundant system restarts and cutover rehearsals. Controlled Risk Profile: Allows teams to validate workloads iteratively in distinct, measurable stages. Why This Is Optimal This strategy directly follows foundational migration best practices for Google Cloud. It balances technical network dependencies with business constraints and operational efficiency, ensuring minimal business disruption and avoiding redundant power cycles.

Professional Cloud Architect

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Grouping Multi-Tier Apps into Migration Waves

Section: Assessing and migrating systems and data to support the solution (e.g., Google Cloud Migration Center)

...

Explanations

...

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A data engineering team is developing an Apache Beam streaming pipeline deployed on Google Cloud Dataflow. The pipeline ingests high-throughput user activity records from Pub/Sub, processes and enriches the records using custom transforms, and writes the output dataset to Cloud Spanner. During testing and transient worker retries, several pipeline workers fail with com.google.cloud.spanner.SpannerException: ALREADY_EXISTS errors, leading to stalled pipeline execution. Which pipeline mutation logic should the engineering team implement to resolve these write failures?

Processing logic

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Last updated August 28, 2026

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Professional Cloud Architect

Provider: Google Cloud
Passing score: 80%
Questions: 50
Duration: 90 minutes

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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

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Grouping Multi-Tier Apps into Migration Waves

Section: Assessing and migrating systems and data to support the solution (e.g., Google Cloud Migration Center)

An enterprise is planning the phased migration of several multi-tier enterprise applications from an on-premises VMware data center to Google Cloud. A comprehensive dependency assessment reveals the following constraints:

Application Dependencies: Presentation and application tiers communicate heavily with backend database servers and require minimal inter-tier latency.
Maintenance Windows: Specific business units permit application switchovers only during pre-approved, scheduled maintenance intervals.
Operational Overhead: Several virtual machines have accumulated pending operating system updates that will require a host reboot.

How should the cloud architect organize the migration waves and execution strategy to minimize downtime and operational disruption?

...

Migrate virtual machines individually in alphabetical order across daily batches, rebooting each host immediately before migration replication starts

...

Refactor backend databases to a globally distributed managed database in wave one, then migrate frontend and application tiers in separate waves during off-peak hours

...

Group interdependent tiers into common migration waves based on dependency mappings and maintenance schedules, aligning pending OS update reboots with the migration switchover

...

Replatform frontend and application servers to containers first while maintaining on-premises database connections until the final wave

...

Explanations

What Migration Wave Planning Entails

Migration wave planning is the systematic grouping and sequencing of workloads during a cloud migration. Rather than moving systems at random or all at once, architects evaluate application dependency mappings, technical constraints, and business operational windows to bundle workloads into logical, risk-managed batches.

How It Meets the Requirements

Application Dependencies: Grouping tightly coupled tiers (such as web, application, and database servers) within the same migration wave prevents high-latency cross-environment traffic (latency between on-premises and Google Cloud) and avoids network bottlenecks across hybrid interconnects.
Maintenance Windows: Aligning each wave with existing business unit maintenance schedules ensures switchovers occur during agreed periods of low transactional activity.
Minimizing Operational Overhead: Aligning pending operating system updates with the scheduled switchover reboot consolidates two necessary power cycles into a single maintenance event, reducing system downtime.

Key Advantages and Capabilities

Reduced Inter-Tier Latency: Prevents degradation of multi-tier applications by migrating interdependent services simultaneously.
Minimized Business Disruption: Adheres to enterprise change management windows and Service Level Objectives (SLOs).
Efficient Resource Utilization: Streamlines administrative overhead by avoiding redundant system restarts and cutover rehearsals.
Controlled Risk Profile: Allows teams to validate workloads iteratively in distinct, measurable stages.

Why This Is Optimal

This strategy directly follows foundational migration best practices for Google Cloud. It balances technical network dependencies with business constraints and operational efficiency, ensuring minimal business disruption and avoiding redundant power cycles.

...

View source documentation

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A data engineering team is developing an Apache Beam streaming pipeline deployed on Google Cloud Dataflow. The pipeline ingests high-throughput user activity records from Pub/Sub, processes and enriches the records using custom transforms, and writes the output dataset to Cloud Spanner. During testing and transient worker retries, several pipeline workers fail with com.google.cloud.spanner.SpannerException: ALREADY_EXISTS errors, leading to stalled pipeline execution. Which pipeline mutation logic should the engineering team implement to resolve these write failures?

Processing logic

Want more questions like this?

Get a free certification question every week.

Last updated August 28, 2026

Are you ready for the Professional Cloud Architect?

50 questions in the full practice test

More Assessing and migrating systems and data to support the solution (e.g., Google Cloud Migration Center) practice questions

Get a free certification question with a full explanation every week.

Professional Cloud Architect

Provider: Google Cloud
Passing score: 80%
Questions: 50
Duration: 90 minutes

Other sections in this exam

Ask Scout

Still curious? Scout, our AI tutor, can explain this concept further and answer your follow-up questions.

An enterprise is planning the phased migration of several multi-tier enterprise applications from an on-premises VMware data center to Google Cloud. A comprehensive dependency assessment reveals the following constraints:

Application Dependencies: Presentation and application tiers communicate heavily with backend database servers and require minimal inter-tier latency.
Maintenance Windows: Specific business units permit application switchovers only during pre-approved, scheduled maintenance intervals.
Operational Overhead: Several virtual machines have accumulated pending operating system updates that will require a host reboot.