Professional-Cloud-DevOps-Engineer 無料問題集「Google Cloud Certified - Professional Cloud DevOps Engineer」

（A）Ensure that all postmortems include how the incident was resolved and what caused the incident without naming customer information.

（B）Ensure that all postmortems include what caused the incident, how the incident could have been worse, and how to prevent a future occurrence of the incident.

（C）Ensure that all postmortems include what caused the incident, identify the person or team responsible forcausing the incident. and how to prevent a future occurrence of the incident.

（D）Ensure that all postmortems include all incident participants in postmortem authoring and share postmortems as widely as possible,

（E）Ensure that all postmortems include the severity of the incident, how to prevent a future occurrence of the incident. and what caused the incident without naming internal system components.

（A）Set up the Kubernetes Engine clusters as private clusters.

（B）Enable Vulnerability Analysis on the Container Registry.

（C）Set up the Kubernetes Engine clusters with Binary Authorization.

（D）Enable Cloud Security Scanner on the clusters.

（A）Provide the partner team with the dashboard URL to enable the partner team to create a copy of the dashboard

（B）Export the metrics to BigQuery Use Looker Studio to create a dashboard, and share the dashboard with the partner team

（C）Download the JSON definition of the dashboard, and send the JSON file to the partner team

（D）Copy the Monitoring Query Language (MQL) query from the dashboard; and send the MQL query to the partner team

（A）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

（B）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.

（C）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

（D）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

（A）Configure Cloud Build with a Terraform builder to execute plan and apply commands.

（B）Create a Job resource with a Terraform docker image to execute terraforrm plan and terraform apply commands.

（C）Install and configure Config Connector in Google Kubernetes Engine (GKE).

（D）Create a Pod resource with a Terraform docker image to execute terraform plan and terraform apply commands.

（A）* Provide a secure file transfer protocol (SFTP) server on a Compute Engine instance so that third parties can upload batches of data and provide appropriate credentials to the server
* Create a Cloud Function with a google.storage, object, finalize Cloud Storage trigger Write code so that the function can scale up a Compute Engine autoscaling managed instance group
* Use an image pre-loaded with the data processing software that terminates the instances when processing completes

（B）* Provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate Identity and Access Management (1AM) access to the bucket
* Use Cloud Monitoring to detect new batches of data in the bucket and trigger a Cloud Function that processes the data
* Set a Cloud Function to use the largest CPU possible to minimize the runtime of the processing

（C）* Provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate Identity and Access Management (1AM) access to the bucket
* Use a standard Google Kubernetes Engine (GKE) cluster and maintain two services one that processes the batches of data and one that monitors Cloud Storage for new batches of data
* Stop the processing service when there are no batches of data to process

（D）* Provide a Cloud Storage bucket so that third parties can upload batches of data, and provide appropriate identity and Access Management (1AM) access to the bucket
* Create a Cloud Function with a google, storage, object .finalise Cloud Storage trigger Write code so that the function can scale up a Compute Engine autoscaling managed instance group
* Use an image pre-loaded with the data processing software that terminates the instances when processing completes

（A）O Examine the latency time, the wall-clock time, and the CPU time of the application. If the latency time is slowly burning down the error budget, and the difference between wall-clock time and CPU time is minimal, mark the application for optimization.

（B）Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the memory resource allocation.

（C）Examine the heap usage Of the application. If the usage is low, mark the application for optimization.

（D）17 Examine the wall-clock time and the CPU time of the application. If the difference is substantial, increase the local disk storage allocation.

（E）Examine the wall-clock time and the CPU time Of the application. If the difference is substantial, increase the CPU resource allocation.

（A）Configure CPU to be allocated only during request processing.

（B）Configure CPU to be always-allocated.

（C）Increase the CPU limit in Cloud Run from 2 to 4.

（D）Use an HTTP health check.

（A）Deploy the new version of the service to the staging environment with a new-release tag without serving traffic Test the new-release version If the test passes; gradually roll out this tagged version Repeat for the production environment

（B）Deploy the new version of the service to the staging environment Split the traffic, and allow 1 % of traffic through to the latest version Test the latest version If the test passes gradually roll out the latest version to the staging and production environments

（C）Deploy the new version of the service to the staging environment Split the traffic, and allow 50% of traffic through to the latest version Test the latest version If the test passes, send all traffic to the latest version Repeat for the production environment

（D）Deploy a new environment with the green tag to use as the staging environment Deploy the new version of the service to the green environment and test the new version If the tests pass, send all traffic to the green environment and delete the existing staging environment Repeat for the production environment

（A）Set up Container Analysis to scan and report Common Vulnerabilities and Exposures.

（B）Configure the containers in the build pipeline to always update themselves before release.

（C）Reconfigure the existing operating system vulnerability software to exist inside the container.

（D）Implement static code analysis tooling against the Docker files used to create the containers.