Associate-Cloud-Engineer Exam Dumps - Google Cloud Certified Questions and Answers

Set autoscaling to On, set the minimum number of instances to 1, and then set the maximum number of instances to 1.

Set autoscaling to Off, set the minimum number of instances to 1, and then set the maximum number of instances to 1.

Set autoscaling to On, set the minimum number of instances to 1, and then set the maximum number of instances to 2.

Set autoscaling to Off, set the minimum number of instances to 1, and then set the maximum number of instances to 2.

Change the default region property setting in the existing GCP project to asia-northeast1.

Change the region property setting in the existing App Engine application from us-central to asia-northeast1.

Create a second App Engine application in the existing GCP project and specify asia-northeast1 as the region to serve your application.

Create a new GCP project and create an App Engine application inside this new project. Specify asia-northeast1 as the region to serve your application.

Create Java packages that utilize the Google Cloud Client Libraries for Java to configure Google Cloud products. Store and share the packages in a source code repository.

Create bash scripts that utilize the Google Cloud CLI to configure Google Cloud products. Store and share the bash scripts in a source code repository.

Create Terraform modules that utilize the Google Cloud Terraform Provider to configure Google Cloud products. Store and share the modules in a source code repository.

Use the Google Cloud APIs by using curl to configure Google Cloud products. Store and share the curl commands in a source code repository.

For each GCP product in the solution, review the pricing details on the products pricing page. Use the pricing calculator to total the monthly costs for each GCP product.

For each GCP product in the solution, review the pricing details on the products pricing page. Create a Google Sheet that summarizes the expected monthly costs for each product.

Provision the solution on GCP. Leave the solution provisioned for 1 week. Navigate to the Billing Report page in the Google Cloud Platform Console. Multiply the 1 week cost to determine the monthly costs.

Provision the solution on GCP. Leave the solution provisioned for 1 week. Use Stackdriver to determine the provisioned and used resource amounts. Multiply the 1 week cost to determine the monthly costs.

Use kubect1 to delete the topic resource.

Use gcloud CLI to delete the topic.

Use kubect1 to create the label deleted-by-cnrm and to change its value to true for the topic resource.

Use gcloud CLI to update the topic label managed-by-cnrm to false.

Review details of the myapp-service Service object and check for error messages.

Review details of the myapp-deployment Deployment object and check for error messages.

Review details of myapp-deployment-58ddbbb995-lp86m Pod and check for warning messages.

View logs of the container in myapp-deployment-58ddbbb995-lp86m pod and check for warning messages.

1. Update your instances’ metadata to add the following value: snapshot–schedule: 0 1 * * *

2. Update your instances’ metadata to add the following value: snapshot–retention: 30

1. In the Cloud Console, go to the Compute Engine Disks page and select your instance’s disk.

2. In the Snapshot Schedule section, select Create Schedule and configure the following parameters:

–Schedule frequency: Daily

–Start time: 1:00 AM – 2:00 AM

–Autodelete snapshots after 30 days

1. Create a Cloud Function that creates a snapshot of your instance’s disk.

2.Create a Cloud Function that deletes snapshots that are older than 30 days.

3.Use Cloud Scheduler to trigger both Cloud Functions daily at 1:00 AM.

1. Create a bash script in the instance that copies the content of the disk to Cloud Storage.

2.Create a bash script in the instance that deletes data older than 30 days in the backup Cloud Storage bucket.

3.Configure the instance’s crontab to execute these scripts daily at 1:00 AM.

Assign the pods of the image rendering microservice a higher pod priority than the older microservices

Create a node pool with compute-optimized machine type nodes for the image rendering microservice Use the node pool with general-purpose

machine type nodes for the other microservices

Use the node pool with general-purpose machine type nodes for lite mage rendering microservice Create a nodepool with compute-optimized machine type nodes for the other microservices

Configure the required amount of CPU and memory in the resource requests specification of the image rendering microservice deployment Keep the resource requests for the other microservices at the default

Add the cluster’s API as a new Type Provider in Deployment Manager, and use the new type to create the DaemonSet.

Use the Deployment Manager Runtime Configurator to create a new Config resource that contains the DaemonSet definition.

With Deployment Manager, create a Compute Engine instance with a startup script that uses kubectl to create the DaemonSet.

In the cluster’s definition in Deployment Manager, add a metadata that has kube-system as key and the DaemonSet manifest as value.

Use your existing CI/CD pipeline Use the generated Docker images and deploy them to Cloud Run. Update the configurations and the required endpoints.

Use your existing continuous integration and delivery (CI/CD) pipeline. Use the generated Docker images and deploy them to Cloud Function. Use the same configuration as on-premises.

Use the existing codebase and deploy each service as a separate Cloud Function Update the configurations and the required endpoints.

Use your existing codebase and deploy each service as a separate Cloud Run Use the same configurations as on-premises.