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QuestionQ247

Deploying applications

A new version of your containerized application has been tested and is ready for production deployment on Google Kubernetes Engine. You could not fully load-test the new version in pre-production environments, and you must ensure it has no performance issues after deployment. The deployment must be automated. What should you do?

  • 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.
Explanation

A canary deployment in a continuous delivery pipeline progressively releases the new application version to a subset of production traffic while monitoring its performance. Traffic can be increased only as the observed metrics support the rollout, limiting the impact of any performance regression. Cloud Deploy supports automated canary progression and can use Google Cloud Observability metrics for rollout analysis.

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