GCP Professional Cloud Architect Practice Question
A retail company is decomposing its on-premises monolithic e-commerce application into containerized microservices that will run on multi-region GKE clusters. Development teams aim to ship new capabilities every week, but their current software development life cycle is a quarterly, waterfall process with manual QA gates. As the cloud architect, which change to the SDLC will most effectively exploit the new cloud-native architecture while maintaining release quality?
Keep the existing waterfall SDLC but migrate the staging and production environments to managed instance groups on Google Cloud for better scalability.
Replace pre-production testing with chaos engineering experiments executed only in production to detect issues after deployment.
Adopt trunk-based development with automated CI pipelines that build, test, and publish versioned container images to Artifact Registry for every commit, enabling continuous delivery of each microservice.
Introduce two-week code-freeze periods before each sprint to coordinate microservice dependencies and perform manual integration testing.
Cloud-native microservices favor small, independent code bases that are updated frequently. Trunk-based development combined with automated CI pipelines allows every commit to be built, tested, and packaged as a versioned container image in Artifact Registry. This shortens feedback loops, supports independent service releases, and enables continuous delivery to GKE. Introducing long code-freeze periods (freeze-then-sprint) contradicts the goal of rapid, iterative delivery. Simply moving the same waterfall process to Google Cloud infrastructure does not address the agility bottleneck. Skipping pre-production testing and relying solely on chaos experiments increases risk and does not ensure baseline functional quality. Therefore, adopting trunk-based development with automated CI/CD is the most impactful first step.
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