You are configuring connectivity across Google Kubernetes Engine (GKE) clusters in different VPCs You notice that the nodes in Cluster A are unable to access the nodes in Cluster B You suspect that the workload access issue is due to the network configuration You need to troubleshoot the issue but do not have execute access to workloads and nodes You want to identify the layer at which the network connectivity is broken What should you do?

• A.Install a toolbox container on the node in Cluster A Confirm that the routes to Cluster B are configured appropriately
• B.Use Network Connectivity Center to perform a Connectivity Test from Cluster A to Cluster
• C.Use a debug container to run the traceroute command from Cluster A to Cluster B and from Cluster B to Cluster A Identify the common failure point
• D.Enable VPC Flow Logs in both VPCs and monitor packet drops

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You have an application that runs on Cloud Run. You want to use live production traffic to test a new version of the application while you let the quality assurance team perform manual testing. You want to limit the potential impact of any issues while testing the new version, and you must be able to roll back to a previous version of the application if needed. How should you deploy the new version?
Choose 2 answers

• A.Deploy a new Cloud Run revision with a tag and use the -no-traffic option.
• B.Deploy a new Cloud Run revision without a tag and use the -no-traffic option.
• C.Deploy the new application version and use the -no-traffic option Route production traffic to the revision's URL.
• D.Deploy the new application version and split traffic to the new version.
• E.Deploy the application as a new Cloud Run service.

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You support an e-commerce application that runs on a large Google Kubernetes Engine (GKE) cluster deployed on-premises and on Google Cloud Platform. The application consists of microservices that run in containers. You want to identify containers that are using the most CPU and memory. What should you do?

• A.Use Stackdriver Kubernetes Engine Monitoring.
• B.Use Prometheus to collect and aggregate logs per container, and then analyze the results in Grafana.
• C.Use the Stackdriver Monitoring API to create custom metrics, and then organize your containers using groups.
• D.Use Stackdriver Logging to export application logs to BigOuery. aggregate logs per container, and then analyze CPU and memory consumption.

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Your organization is starting to containerize with Google Cloud. You need a fully managed storage solution for container images and Helm charts. You need to identify a storage solution that has native integration into existing Google Cloud services, including Google Kubernetes Engine (GKE), Cloud Run, VPC Service Controls, and Identity and Access Management (IAM). What should you do?

• A.Use Docker to configure a Cloud Storage driver pointed at the bucket owned by your organization.
• B.Configure Artifact Registry as an OCI-based container registry for both Helm charts and container images.
• C.Configure Container Registry as an OCI-based container registry for container images.
• D.Configure an open source container registry server to run in GKE with a restrictive role-based access control (RBAC) configuration.

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You use a multiple step Cloud Build pipeline to build and deploy your application to Google Kubernetes Engine (GKE). You want to integrate with a third-party monitoring platform by performing a HTTP POST of the build information to a webhook. You want to minimize the development effort. What should you do?

• A.Add logic to each Cloud Build step to HTTP POST the build information to a webhook.
• B.Use Stackdriver Logging to create a logs-based metric from the Cloud Build logs. Create an Alert with a Webhook notification type.
• C.Add a new step at the end of the pipeline in Cloud Build to HTTP POST the build information to a webhook.
• D.Create a Cloud Pub/Sub push subscription to the Cloud Build cloud-builds PubSub topic to HTTP POST the build information to a webhook.

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