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Answer: 1. Install the Anthos Service Mesh on your GKE cluster. 2. Create two Deployments on the GKE cluster, and label them with different version names. 3. Implement an Istio routing rule to send a small percentage of traffic to the Deployment that references the new version of the application.

The question requires a deployment strategy that ensures no downtime and allows A/B testing by directing a subset of traffic to the new version. Option C leverages Anthos Service Mesh (Istio) to split traffic between two versions using Istio routing rules. This meets both requirements: no downtime and controlled traffic distribution for A/B testing.

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Quick Answer

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Author: LeetQuiz Editorial Team

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How can you deploy an internet-facing microservices application on Google Kubernetes Engine (GKE) to support A/B testing of new features while meeting the following requirements?

• Ensure zero downtime during deployments of new container images
• Validate new production releases with a subset of production users

What is the recommended approach?

Exam-Like

Configure your CI/CD pipeline to update the Deployment manifest file by replacing the container version with the latest version. 2. Recreate the Pods in your cluster by applying the Deployment manifest file. 3. Validate the application's performance by comparing its functionality with the previous release version, and roll back if an issue arises.

7.1%

Create a second namespace on GKE for the new release version. 2. Create a Deployment configuration for the second namespace with the desired number of Pods. 3. Deploy new container versions in the second namespace. 4. Update the Ingress configuration to route traffic to the namespace with the new container versions.

14.3%

Install the Anthos Service Mesh on your GKE cluster. 2. Create two Deployments on the GKE cluster, and label them with different version names. 3. Implement an Istio routing rule to send a small percentage of traffic to the Deployment that references the new version of the application.

57.1%

Implement a rolling update pattern by replacing the Pods gradually with the new release version. 2. Validate the application's performance for the new subset of users during the rollout, and roll back if an issue arises.

21.4%