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You are managing a new project for a web application on Google Cloud. The web application is running on a Compute Engine VM, and your team has a strict budget. You have already created a budget in Cloud Billing and set an alert rule to send an email when 90% of the budget is consumed. You must ensure that project spending automatically stops if it exceeds the budgeted amount. What should you do?

A.

Create a new Cloud Billing account, and associate the project with it to prevent new spending from affecting your project ' s budget.

B.

Create a Pub/Sub topic, and connect it to the budget. Create a Cloud Run function that subscribes to the Pub/Sub topic and executes code to stop the VM.

C.

Use the Cloud Billing dashboard to identify the forecasted spend of your project, and then update the budget and alert to match the forecasted spend.

D.

Rely on the budget ' s configuration to automatically disable billable resources when the spend reaches 100%.

You need to monitor resources that are distributed over different projects in Google Cloud Platform. You want to consolidate reporting under the same Stackdriver Monitoring dashboard. What should you do?

A.

Use Shared VPC to connect all projects, and link Stackdriver to one of the projects.

B.

For each project, create a Stackdriver account. In each project, create a service account for that project and grant it the role of Stackdriver Account Editor in all other projects.

C.

Configure a single Stackdriver account, and link all projects to the same account.

D.

Configure a single Stackdriver account for one of the projects. In Stackdriver, create a Group and add the other project names as criteria for that Group.

You are storing sensitive information in a Cloud Storage bucket. For legal reasons, you need to be able to record all requests that read any of the stored data. You want to make sure you comply with these requirements. What should you do?

A.

Enable the Identity Aware Proxy API on the project.

B.

Scan the bucker using the Data Loss Prevention API.

C.

Allow only a single Service Account access to read the data.

D.

Enable Data Access audit logs for the Cloud Storage API.

Your company is moving its continuous integration and delivery (CI/CD) pipeline to Compute Engine instances. The pipeline will manage the entire cloud infrastructure through code. How can you ensure that the pipeline has appropriate permissions while your system is following security best practices?

A.

• Add a step for human approval to the CI/CD pipeline before the execution of the infrastructureprovisioning.• Use the human approvals IAM account for the provisioning.

B.

• Attach a single service account to the compute instances.• Add minimal rights to the service account.• Allow the service account to impersonate a Cloud Identity user with elevated permissions to create, update, or delete resources.

C.

• Attach a single service account to the compute instances.• Add all required Identity and Access Management (IAM) permissions to this service account to create, update, or delete resources

D.

• Create multiple service accounts, one for each pipeline with the appropriate minimal Identity andAccess Management (IAM) permissions.• Use a secret manager service to store the key files of the service accounts.• Allow the CI/CD pipeline to request the appropriate secrets during the execution of the pipeline.

(You are managing a stateful application deployed on Google Kubernetes Engine (GKE) that can only have one replica. You recently discovered that the application becomes unstable at peak times. You have identified that the application needs more CPU than what has been configured in the manifest at these peak times. You want Kubernetes to allocate the application sufficient CPU resources during these peak times, while ensuring cost efficiency during off-peak periods. What should you do?)

A.

Enable cluster autoscaling on the GKE cluster.

B.

Configure a Vertical Pod Autoscaler on the Deployment.

C.

Configure a Horizontal Pod Autoscaler on the Deployment.

D.

Enable node auto-provisioning on the GKE cluster.

Your preview application, deployed on a single-zone Google Kubernetes Engine (GKE) cluster in us-centrall, has gained popularity. You are now ready to make the application generally available. You need to deploy the application to production while ensuring high availability and resilience. You also want to follow Google-recommended practices. What should you do?

A.

Use the gcloud container clusters create command with the options--enable-multi-networking and--enable- autoscaling to create an autoscaling zonal cluster and deploy the application to it.

B.

Use the gcloud container clusters create-auto command to create an autopilot cluster and deploy the application to it.

C.

Use the gcloud container clusters update command with the option—region us-centrall to update the cluster and deploy the application to it.

D.

Use the gcloud container clusters update command with the option—node-locations us-centrall-a,us-centrall-b to update the cluster and deploy the application to the nodes.

You are running multiple microservices in a Kubernetes Engine cluster. One microservice is rendering images. The microservice responsible for the image rendering requires a large amount of CPU time compared to the memory it requires. The other microservices are workloads that are optimized for n1-standard machine types. You need to optimize your cluster so that all workloads are using resources as efficiently as possible. What should you do?

A.

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

B.

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

C.

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

D.

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

You created a Kubernetes deployment by running kubectl run nginx image=nginx replicas=1. After a few days, you decided you no longer want this deployment. You identified the pod and deleted it by running kubectl delete pod. You noticed the pod got recreated.

$ kubectlgetpods

NAME READY STATUS RESTARTS AGE

nginx-84748895c4-nqqmt 1/1 Running 0 9m41s

$ kubectldeletepod nginx-84748895c4-nqqmt

pod nginx-84748895c4-nqqmt deleted

$ kubectlgetpods

NAME READY STATUS RESTARTS AGE

nginx-84748895c4-k6bzl 1/1 Running 0 25s

What should you do to delete the deployment and avoid pod getting recreated?

A.

kubectl delete deployment nginx

B.

kubectl delete –deployment=nginx

C.

kubectl delete pod nginx-84748895c4-k6bzl –no-restart 2

D.

kubectl delete inginx

You have 32 GB of data in a single file that you need to upload to a Nearline Storage bucket. The WAN connection you are using is rated at 1 Gbps, and you are the only one on the connection. You want to use as much of the rated 1 Gbps as possible to transfer the file rapidly. How should you upload the file?

A.

Use the GCP Console to transfer the file instead of gsutil.

B.

Enable parallel composite uploads using gsutil on the file transfer.

C.

Decrease the TCP window size on the machine initiating the transfer.

D.

Change the storage class of the bucket from Nearline to Multi-Regional.

Your company is closely monitoring their cloud spend. You need to allow different teams to monitor their Google Cloud costs. You must ensure that team members receive notifications when their cloud spend reaches certain thresholds and give team members the ability to create dashboards for additional insights with detailed billing data. You want to follow Google-recommended practices and minimize engineering costs. What should you do?

A.

Set up alerts for each team based on required thresholds. Set up billing exports to BigQuery. Grant team members access to BigQuery.

B.

Set up alerts for each team based on required thresholds. Create a shell script to read data from the Cloud Billing API, and push the results to BigQuery. Grant team members access to BigQuery.

C.

Deploy Grafana to Compute Engine. Create a dashboard for each team that uses the data from the Cloud Billing API. Ask each team to create their own alerts in Cloud Monitoring.

D.

Deploy Grafana to Compute Engine. Create a dashboard for each team that uses the data from the Cloud Billing Budget API. Ask each team to create their own alerts in Grafana.