Google Professional-Cloud-DevOps-Engineer Exam With Confidence Using Practice Dumps

Use the default Stackdriver Kubernetes Engine Monitoring agent configuration.

Deploy a Fluentd daemonset to GKE. Then create a customized input and output configuration to tail the log file in the application's pods and write to Slackdriver Logging.

Install Kubernetes on Google Compute Engine (GCE> and redeploy your applications. Then customize the built-in Stackdriver Logging configuration to tail the log file in the application's pods and write to Stackdriver Logging.

Write a script to tail the log file within the pod and write entries to standard output. Run the script as a sidecar container with the application's pod. Configure a shared volume between the containers to allow the script to have read access to /var/log in the application container.

Deploy the application through a continuous delivery pipeline by using canary deployments Use Cloud Monitoring to look for performance issues, and ramp up traffic as supported by the metrics

Deploy the application through a continuous delivery pipeline by using blue/green deployments Migrate traffic to the new version of the application and use Cloud Monitoring to look for performance issues

Deploy the application by using kubectl and use Config Connector to slowly ramp up traffic between versions. Use Cloud Monitoring to look for performance issues

Deploy the application by using kubectl and set the spec. updatestrategy. type field to RollingUpdate Use Cloud Monitoring to look for performance issues, and run the kubectl rollback command if there are any issues.

Roll back the experimental canary release.

Start monitoring latency, traffic, errors, and saturation.

Record data for the postmortem document of the incident.

Trace the origin of 500 errors and the root cause of increased latency.