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PROFESSIONAL-CLOUD-DEVOPS-ENGINEER · Question #62

The new version of your containerized application has been tested and is ready to be deployed to production on Google Kubernetes Engine (GKE). You could not fully load-test the new version in your…

The correct answer is A. Deploy the application through a continuous delivery pipeline by using canary deployments. Use. To mitigate deployment risks from insufficient load testing for a new GKE application version, use a continuous delivery pipeline with canary deployments.

Submitted by lucia.co· Apr 18, 2026Applying site reliability engineering principles to a service

Question

The new version of your containerized application has been tested and is ready to be deployed to production on Google Kubernetes Engine (GKE). You could not fully load-test the new version in your pre-production environment, and you need to ensure that the application does not have performance problems after deployment. Your deployment must be automated. What should you do?

Options

  • ADeploy the application through a continuous delivery pipeline by using canary deployments. Use
  • BDeploy the application through a continuous delivery pipeline by using blue/green deployments.
  • CDeploy the application by using kubectl and use Config Connector to slowly ramp up traffic
  • DDeploy the application by using kubectl and set the spec.updateStrategy.type field to

How the community answered

(26 responses)
  • A
    77% (20)
  • B
    8% (2)
  • C
    4% (1)
  • D
    12% (3)

Why each option

To mitigate deployment risks from insufficient load testing for a new GKE application version, use a continuous delivery pipeline with canary deployments.

ADeploy the application through a continuous delivery pipeline by using canary deployments. UseCorrect

Canary deployments gradually roll out the new application version to a small subset of users or traffic first, allowing real-world performance monitoring and validation before a full rollout. This minimizes the impact of potential performance issues that were not caught during pre-production load testing.

BDeploy the application through a continuous delivery pipeline by using blue/green deployments.

Blue/green deployments switch all traffic to the new version simultaneously, which carries a higher risk of performance degradation if the new version has issues and wasn't fully load-tested.

CDeploy the application by using kubectl and use Config Connector to slowly ramp up traffic

Using `kubectl` is a manual or script-based approach, not a full continuous delivery pipeline, and Config Connector is for resource management, not dynamic traffic shifting for new deployments.

DDeploy the application by using kubectl and set the spec.updateStrategy.type field to

Setting `spec.updateStrategy.type` in Kubernetes controls how pods are updated (e.g., rolling update), but it doesn't provide the granular traffic shifting and monitoring capabilities of a canary deployment for risk mitigation.

Concept tested: Canary deployments for risk mitigation

Source: https://cloud.google.com/architecture/application-deployment-strategies#canary_deployments

Topics

#Canary deployments#Continuous delivery#Deployment strategies#Risk management

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