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Answer: Deploy the application via a continuous delivery pipeline using canary deployments. Use Cloud Monitoring to look for performance issues. and ramp up traffic as the metrics support it.
When deploying a new version to Google Kubernetes Engine (GKE) without full pre-production load testing, the safest and most automated approach is to use a canary deployment in a continuous delivery pipeline. A canary deployment gradually routes a small percentage of production traffic to the new version while keeping the old version running. This allows you Monitor performance in real time using Cloud Monitoring. Increase traffic gradually only if metrics (latency, error rate, CPU/memory usage) remain healthy. Roll back quickly if issues are detected, minimizing impact on users. This approach reduces risk, supports automation, and follows Google Cloud best practices for safe production rollouts.
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You have a containerized application with a new version that has been tested and is ready for production deployment on Google Kubernetes Engine. However, full load testing was not completed in pre-production environments, and you need to ensure the deployment does not introduce performance issues. The deployment process must be automated. What is the recommended approach?
A
Use Cloud Load Balancing to slowly ramp up traffic between versions. Use Cloud Monitoring to look for performance issues.
B
Deploy the application via a continuous delivery pipeline using canary deployments. Use Cloud Monitoring to look for performance issues. and ramp up traffic as the metrics support it.
C
Deploy the application via a continuous delivery pipeline using blue/green deployments. Use Cloud Monitoring to look for performance issues, and launch fully when the metrics support it.
D
Deploy the application using kubectl and set the spec.updateStrategv.type to RollingUpdate. Use Cloud Monitoring to look for performance issues, and run the kubectl rollback command if there are any issues.