GCP Professional Cloud Architect Practice Question
During a phased migration of several hundred microservices to Google Kubernetes Engine, roughly 15-20 percent of the Cloud Build jobs fail because of missing Maven dependencies or integration-test mismatches. The operations lead asks how an AI-assisted migration tool such as CogniPort can be used to shorten recovery time without replacing the existing CI/CD tooling. What should you recommend?
Add a CogniPort post-build step in the existing Cloud Build pipeline so it analyzes compilation and test logs on every run and opens pull requests with suggested fixes.
Invoke CogniPort during the kubectl apply command so it can rewrite Kubernetes manifests before they are deployed to the cluster.
Run CogniPort only once at the start of the migration to create an application inventory and then disable it to avoid runtime overhead.
Replace Cloud Build completely with CogniPort's native build service so that all compilation and tests run inside CogniPort.
CogniPort is designed to parse build and test error output and generate automated remediation suggestions-such as updated dependency declarations or corrected test stubs-based on each run's logs. Adding a post-build CogniPort step inside the current Cloud Build pipeline lets it analyze every compilation and test log, open pull requests with proposed fixes, and keep the normal code-review workflow intact, thereby accelerating recovery from failures without changing the team's build system. Although CogniPort's native build service could also speed recovery, that approach would replace Cloud Build, contradicting the stated requirement. Running CogniPort only once or invoking it during kubectl apply would not provide continuous, code-level remediation.
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GCP Professional Cloud Architect
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