Google Associate-Cloud-Engineer Dumps

Stream data to Pub/Sub, and use Dataflow to send data to Cloud Storage

Stream data to Pub/Sub. and use Storage Transfer Service to send data to BigQuery.

Stream data to Dataflow, and use Storage Transfer Service to send data to BigQuery.

Stream data to Dataflow, and use Dataprep by Trifacta to send data to Bigtable.

Google Compute Engine

Google App Engine

Cloud Functions

Google Kubernetes Engine

Option A

Option B

Option C

Option D

In the Google Cloud console for your organization, select Create role from selection, and choose destination as the startup company's organization

In the Google Cloud console for the startup company, select Create role from selection and choose source as the startup company's Google Cloud organization.

Use the gcloud iam roles copy command, and provide the Organization ID of the startup company'sGoogle Cloud Organization as the destination.

Use the gcloud iam roles copy command, and provide the project IDs of all projects in the startup company s organization as the destination.

Cloud SQL

Cloud Spanner

Cloud Firestore

Cloud Datastore

Build the solution by using YAML manifests, and provision the resources.

Build the solution by using Terraform, and provision the resources.

Build the solution by using Python and the cloud SDKs from all providers to provision the resources.

Build the solution by using Config Connector, and provision the resources.

Configure an HTTP(S) load balancer.

Configure an internal TCP load balancer.

Configure an external SSL proxy load balancer.

Configure an external TCP proxy load balancer.

Use a Network Load Balancer

Deploy your VMs in multiple Google Cloud regions closest to your users' geographical locations.

Use an external Application Load Balancer in Global mode.

Use an external Application Load Balancer in Regional mode.

Deploy all of your VMs in a single Google Cloud region with the largest available CIDR range.

Create a GKE Autopilot cluster. Enroll the cluster in the rapid release channel.

Create a GKE Autopilot cluster. Enroll the cluster in the stable release channel.

Create a zonal GKE standard cluster. Enroll the cluster in the stable release channel.

Create a regional GKE standard cluster. Enroll the cluster in the rapid release channel.

• Navigate to Cloud Mentioning in the Google Cloud console, and create a custom monitoring job for theBigtable instance to track all changes.• Create an alert by using webhook endpoints. with the SIEM endpoint as a receiver

• Navigate to the Audit Logs page in the Google Cloud console, and enable Data Read. Data Write and Admin Read logs for the Bigtable instance• Create a Pub/Sub topic as a Cloud Logging sink destination, and add your SIEM as a subscriber to the topic.

• Install the Ops Agent on the Bigtable instance during configuration. K• Create a service account with read permissions for the Bigtable instance.• Create a custom Dataflow job with this service account to export logs to the company's SIEM system.

• Navigate to the Audit Logs page in the Google Cloud console, and enable Admin Write logs for theBiglable instance.• Create a Cloud Functions instance to export logs from Cloud Logging to your SIEM.

Create two configurations using gcloud config configurations create [NAME]. Run gcloud config configurations activate [NAME] to switch between accounts when running the commands to start the Compute Engine instances.

Create two configurations using gcloud config configurations create [NAME]. Run gcloud configurations list to start the Compute Engine instances.

Activate two configurations using gcloud configurations activate [NAME]. Run gcloud config list to start the Compute Engine instances.

Activate two configurations using gcloud configurations activate [NAME]. Run gcloud configurations list to start the Compute Engine instances.

Use traffic splitting

Modify the minimum number of Cloud Run instances.

Set a minimum concurrent requests environment variable for the application.

Increase the maximum number of Cloud Run instances.

Use Google Cloud Directory Sync (GCDS) to synchronize users into Cloud Identity.

Use the cloud Identity APIs and write a script to synchronize users to Cloud Identity.

Export users from Active Directory as a CSV and import them to Cloud Identity via the Admin Console.

Ask each employee to create a Google account using self signup. Require that each employee use their company email address and password.

Run gcloud builds submit --tag gcr.io/img-278322/acme_track_n_trace

Run gcloud builds submit --tag gcr.io/img-278322/acme_track_n_trace:v1

Run gcloud builds submit --tag gcr.io/prod-278986/acme_track_n_trace

Run gcloud builds submit --tag gcr.io/prod-278986/acme_track_n_trace:v1

Configure a Cloud Router in vpc-a and another Cloud Router in vpc-b.

Configure a Cloud Interconnect connection between vpc-a and vpc-b.

Create VPC Network Peering between vpc-a and vpc-b.

Create an OpenVPN connection between vpc-a and vpc-b.

An internal HTTP(S) load balancer together with Identity-Aware Proxy to allow only HTTPS traffic.

An external HTTP(S) load balancer to distribute the load and a URL map to target the requests for the static content to the Cloud Storage backend. Install the HTTPS certificates on the instance.

An external HTTP(S) load balancer with a managed SSL certificate to distribute the load and a URL map to target the requests for the static content to the Cloud Storage backend.

An external network load balancer pointing to the backend instances to distribute the load evenly. The web servers will forward the request to the Cloud Storage as needed.

Set up a low-priority (65534) rule that blocks all egress and a high-priority rule (1000) that allows only the appropriate ports.

Set up a high-priority (1000) rule that pairs both ingress and egress ports.

Set up a high-priority (1000) rule that blocks all egress and a low-priority (65534) rule that allows only the appropriate ports.

Set up a high-priority (1000) rule to allow the appropriate ports.

Deploy the new version in the same application and use the --migrate option.

Deploy the new version in the same application and use the --splits option to give a weight of 99 to the current version and a weight of 1 to the new version.

Create a new App Engine application in the same project. Deploy the new version in that application. Use the App Engine library to proxy 1% of the requests to the new version.

Create a new App Engine application in the same project. Deploy the new version in that application. Configure your network load balancer to send 1% of the traffic to that new application.

Use the gsutil to rewrite the storage class for the bucket Change the default storage class for the bucket

Use the gsutil to rewrite the storage class for the bucket Set up Object Lifecycle management on the bucket

Create a new bucket and change the default storage class for the bucket Set up Object Lifecycle management on lite bucket

Create a new bucket and change the default storage class for the bucket import the files from the previous bucket into the new bucket

Use Spanner for all data.

Use Cloud SQL for customer data, Cloud Storage (Coldline) for historical logs, and BigQuery for sensor data.

Use Cloud SQL for customer data, Cloud Storage (Archive) for historical logs, and Bigtable for sensor data.

Use Firestore for customer data, Cloud Storage (Nearline) for historical logs, and Bigtable for sensor data.

1. Create a Cloud Monitoring Dashboard2. Collect metrics and publish them into the Pub/Sub topics 3. Add CPU and network Charts (or each of (he three projects

1. Create a Cloud Monitoring Dashboard.2. Select the CPU and Network metrics from the three projects.3. Add CPU and network Charts lot each of the three protects.

1 Create a Service Account and apply roles/viewer on the three projects2. Collect metrics and publish them lo the Cloud Monitoring API3. Add CPU and network Charts for each of the three projects.

1. Create a fourth Google Cloud project2 Create a Cloud Workspace from the fourth project and add the other three projects

Create a service account with just the permissions to access files in the bucket. Create a JSON key for the service account. Execute the command gsutil signurl -m 1h gs:///*.

Create a service account with just the permissions to access files in the bucket. Create a JSON key for the service account. Execute the command gsutil signurl -d 1h gs:///.

Create a service account with just the permissions to access files in the bucket. Create a JSON key for the service account. Execute the command gsutil signurl -p 60m gs:///.

Create a JSON key for the Default Compute Engine Service Account. Execute the command gsutil signurl -t 60m gs:///*

Enable Cloud IAP for the Compute Engine instances, and add the operations partner as a Cloud IAP Tunnel User.

Tag all the instances with the same network tag. Create a firewall rule in the VPC to grant TCP access on port 22 for traffic from the operations partner to instances with the network tag.

Set up Cloud VPN between your Google Cloud VPC and the internal network of the operations partner.

Ask the operations partner to generate SSH key pairs, and add the public keys to the VM instances.

Grant the Project Editor role to the Marketing learn for acme data digest

Create a Project Lien on acme-data digest and then grant the Project Editor role to the Marketing team

Create another protect with the ID acme-marketing-data-digest for the Marketing team and deploy the resources there

Create a new protect named Meeting Data Digest and use the ID acme-data-digest Grant the Project Editor role to the Marketing team.

Move both projects under the same folder.

Grant the VM Service Account the role Storage Object Creator on corp-aggregate-reports-storage.

Create a Shared VPC network between both projects. Grant the VM Service Account the role Storage Object Creator on corp-iot-insights.

Make corp-aggregate-reports-storage public and create a folder with a pseudo-randomized suffix name. Share the folder with the IoT team.

Modify the existing subnet range to 172.16.20.0/24.

Create a new Secondary IP Range in the VPC and configure the VMs to use that range.

Create a new VPC network for the VMs. Enable VPC Peering between the VMs’ VPC network and the Dataproc cluster VPC network.

Create a new VPC network for the VMs with a subnet of 172.32.0.0/16. Enable VPC network Peering between the Dataproc VPC network and the VMs VPC network. Configure a custom Route exchange.

Add users to roles/bigquery user role only, instead of roles/bigquery dataOwner.

Add users to roles/bigquery dataEditor role only, instead of roles/bigquery dataOwner.

Create a custom role by removing delete permissions, and add users to that role only.

Create a custom role by removing delete permissions. Add users to the group, and then add the group to the custom role.

Assign the auditor the IAM role roles/logging.privateLogViewer. Perform the export of logs to Cloud Storage.

Assign the auditor the IAM role roles/logging.privateLogViewer. Direct the auditor to also review the logs for changes to Cloud IAM policy.

Assign the auditor’s IAM user to a custom role that has logging.privateLogEntries.listpermission. Perform the export of logs to Cloud Storage.

Assign the auditor’s IAM user to a custom role that has logging.privateLogEntries.listpermission. Direct the auditor to also review the logs for changes to Cloud IAM policy.

* Create service accounts sa-app and sa-db.• Associate service account: sa-app with the application servers and the service account sa-db with the database servers.• Create an ingress firewall rule to allow network traffic from source service account sa-app to target service account sa-db.

• Create network tags app-server and db-server.• Add the app-server lag lo the application servers and the db-server lag to the database servers.• Create an egress firewall rule to allow network traffic from source network tag app-server to target network tag db-server.

* Create a service account sa-app and a network tag db-server.* Associate the service account sa-app with the application servers and the network tag db-server withthe database servers.• Create an ingress firewall rule to allow network traffic from source VPC IP addresses and target the subnet-a IP addresses.

• Create a network lag app-server and service account sa-db.• Add the tag to the application servers and associate the service account with the database servers.• Create an egress firewall rule to allow network traffic from source network tag app-server to target service account sa-db.

Set up Error Reporting to identify stack traces that indicate slowdowns in Dataflow jobs. Set up alerts based on these log entries.

Use the Personalized Service Health dashboard to identify issues with Dataflow jobs across regions.

Update the Dataflow job configurations to send messages to a Pub/Sub topic when there are delays. Configure a backup Dataflow job to process jobs that are delayed. Use Cloud Tasks to trigger an alert when messages are pushed to the Pub/Sub topic.

Set up Cloud Monitoring alerts on the data freshness metric for the Dataflow jobs to receive a notification when a certain threshold is reached.

Create a service account per team, and grant the service account the Project Editor role. Ask the teams to provision their infrastructure through the Google Cloud CLI (gcloud CLI), while impersonating their dedicated service account.

Provide training for all engineering teams you work with to understand the company’s required practices. Allow the engineering teams to provision the infrastructure to best meet their needs.

Configure organization policies to enforce your company’s required practices. Ask the teams to provision their infrastructure by using the Google Cloud console.

Write Terraform modules for each component that are compliant with the company’s required practices, and ask teams to implement their infrastructure through these modules.

Confirm that network firewall rules are not blocking traffic for User A.

Review recent configuration changes that may have caused unintended modifications to permissions.

Verify that User A has the Identity and Access Management (IAM) Project Owner role assigned.

Review the error message that User A received.

Create a new service with the new version of the application. Split traffic between this version and the version that is currently running.

Create a new revision with the new version of the application. Split traffic between this version and the version that is currently running.

Create a new service with the new version of the application. Add an HTTP Load Balancer in front of both services.

Create a new revision with the new version of the application. Add an HTTP Load Balancer in front of both revisions.

In Google Cloud, configure the VPC as a host for Shared VPC.

In Google Cloud, configure the VPC for VPC Network Peering.

Create bastion hosts both in your on-premises environment and on Google Cloud. Configure both as proxy servers using their public IP addresses.

Set up Cloud VPN between the infrastructure on-premises and Google Cloud.

The pending Pod's resource requests are too large to fit on a single node of the cluster.

Too many Pods are already running in the cluster, and there are not enough resources left to schedule the pending Pod.

The node pool is configured with a service account that does not have permission to pull the container image used by the pending Pod.

The pending Pod was originally scheduled on a node that has been preempted between the creation of the Deployment and your verification of the Pods’ status. It is currently being rescheduled on a new node.

Add the users to roles/browser role.

Add the users to roles/iam.roleViewer role.

Add the users to a group, and add this group to roles/browser role.

Add the users to a group, and add this group to roles/iam.roleViewer role.

Add the support team group to the roles/monitoring.viewer role

Add the support team group to the roles/spanner.databaseUser role.

Add the support team group to the roles/spanner.databaseReader role.

Add the support team group to the roles/stackdriver.accounts.viewer role.

Check the app.yaml file for your application and check project settings.

Check the web-application.xml file for your application and check project settings.

Go to Deployment Manager and review settings for deployment of applications.

Go to Cloud Shell and run gcloud config list to review the Google Cloud configuration used for deployment.

Use the BigQuery interface to review the nightly Job and look for any errors

Review the Error Reporting page in the Cloud Console to find any errors.

In Cloud Logging create a filter for your Data Studio report

Use the open source CLI tool. Snapshot Debugger, to find out why the data was not refreshed correctly.

Deploy a private autopilot cluster

Deploy a public autopilot cluster.

Deploy a standard public cluster and enable shielded nodes.

Deploy a standard private cluster and enable shielded nodes.

Create a Kubernetes Service of type NodePort for your application, and a Kubernetes Ingress to expose this Service via a Cloud Load Balancer.

Create a Kubernetes Service of type ClusterIP for your application. Configure the public DNS name of your application using the IP of this Service.

Create a Kubernetes Service of type NodePort to expose the application on port 443 of each node of the Kubernetes cluster. Configure the public DNS name of your application with the IP of every node of the cluster to achieve load-balancing.

Create a HAProxy pod in the cluster to load-balance the traffic to all the pods of the application. Forward the public traffic to HAProxy with an iptable rule. Configure the DNS name of your application using the public IP of the node HAProxy is running on.

Increase the size of the disk to 1 TB.

Increase the allocated CPU to the instance.

Migrate to use a Local SSD on the instance.

Migrate to use a Regional SSD on the instance.

Store the database password inside the Docker image of the container, not in the YAML file.

Store the database password inside a Secret object. Modify the YAML file to populate the DB_PASSWORD environment variable from the Secret.

Store the database password inside a ConfigMap object. Modify the YAML file to populate the DB_PASSWORD environment variable from the ConfigMap.

Store the database password in a file inside a Kubernetes persistent volume, and use a persistent volume claim to mount the volume to the container.

Deploy Jenkins through the Google Cloud Marketplace.

Create a new Compute Engine instance. Run the Jenkins executable.

Create a new Kubernetes Engine cluster. Create a deployment for the Jenkins image.

Create an instance template with the Jenkins executable. Create a managed instance group with this template.

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.

Configure Cloud NAT for all subnets of your VPC to be used when egressing from the VM instances.

Create a private zone on Cloud DNS, and configure the applications with the DNS name.

Configure the IP of the database as custom metadata for each instance, and query the metadata server.

Query the Compute Engine internal DNS from the applications to retrieve the IP of the database.

Grant the financial team the IAM role ofג€Billing Account Userג€ on the billing account linked to your credit card.

Set up BigQuery billing export and grant your financial department IAM access to query the data.

Create a ticket with Google Billing Support to ask them to send the invoice to your company.

Change the billing account of your projects to the billing account of your company.

Provision preemptible Compute Engine instances.

Provision Compute Engine instances with GPUs attached.

Provision Compute Engine instances with local SSDs attached.

Provision Compute Engine instances with M1 machine type.

Disable the flag “Delete boot disk when instance is deleted.”

Enable delete protection on the instance.

Disable Automatic restart on the instance.

Enable Preemptibility on the instance.

Create individual VPCs per Google Cloud project. Peer all the VPCs together. Apply organization policies on the organization level.

Create individual VPCs for each Google Cloud project. Peer all the VPCs together. Apply hierarchical firewall policies on the organization level.

Create a host VPC project with each production project as its service project. Apply organization policies on the organization level.

Create a host VPC project with each production project as its service project. Apply hierarchical firewall policies on the organization level.

Change the subnet IP range from 10.0.0.0/20 to 10.0.0.0/22.

Change the subnet IP range from 10.0 0.0/20 to 10.0.0.0718.

Add a secondary IP range 10.1.0.0/20 to the subnet.

Convert the subnet IP range from IPv4 to IPv6

Configure Cloud Identity-Aware Proxy (or HTTPS resources

Configure Cloud Identity-Aware Proxy for SSH and TCP resources.

Create an SSH keypair and store the public key as a project-wide SSH Key

Create an SSH keypair and store the private key as a project-wide SSH Key

1. Create a configuration for each project you need to manage.2. Activate the appropriate configuration when you work with each of your assigned GCP projects.

1. Create a configuration for each project you need to manage.2. Use gcloud init to update the configuration values when you need to work with a non-default project

1. Use the default configuration for one project you need to manage.2. Activate the appropriate configuration when you work with each of your assigned GCP projects.

1. Use the default configuration for one project you need to manage.2. Use gcloud init to update the configuration values when you need to work with a non-default project.

Create an alert policy to send a notification when the HTTP response latency exceeds the specified threshold.

Implement an App Engine service which invokes the Cloud Monitoring API and sends a notification in case of anomalies.

Use the Cloud Monitoring dashboard to observe latency and take the necessary actions when the response latency exceeds the specified threshold.

Export Cloud Monitoring metrics to BigQuery and use a Looker Studio dashboard to monitor your web applications latency.

1. Create a consumer Gmail account.2.Write a script that monitors the CPU usage.3.When the CPU usage exceeds the threshold, have that script send an email using the Gmail account and smtp.gmail.com on port 25 as SMTP server.

1. Create a Stackdriver Workspace, and associate your Google Cloud Platform (GCP) project with it.2.Create an Alerting Policy in Stackdriver that uses the threshold as a trigger condition.3.Configure your email address in the notification channel.

1. Create a Stackdriver Workspace, and associate your GCP project with it.2.Write a script that monitors the CPU usage and sends it as a custom metric to Stackdriver.3.Create an uptime check for the instance in Stackdriver.

1. In Stackdriver Logging, create a logs-based metric to extract the CPU usage by using this regular expression: CPU Usage: ([0-9] {1,3}) %2.In Stackdriver Monitoring, create an Alerting Policy based on this metric.3.Configure your email address in the notification channel.

HTTPS Load Balancer

Network Load Balancer

SSL Proxy Load Balancer

Internal TCP/UDP Load Balancer. Add a firewall rule allowing ingress traffic from 0.0.0.0/0 on the target instances.

Deploy the container on Cloud Run for Anthos, and set the minimum number of instances to zero

Deploy the container on Cloud Run (fully managed), and set the minimum number of instances to zero.

Deploy the container on App Engine flexible environment with autoscaling. and set the value min_instances to zero in the app yaml

Deploy the container on App Engine flexible environment with manual scaling, and set the value instances to zero in the app yaml

Rely on live migration to move the workload to a machine with more memory.

Use gcloud to add metadata to the VM. Set the key to required-memory-size and the value to 8 GB.

Stop the VM, change the machine type to n1-standard-8, and start the VM.

Stop the VM, increase the memory to 8 GB, and start the VM.

In the Log Viewer, filter the logs on severity 'Error' and the name of the Service Account.

Create a sink to BigQuery to export all the logs. Create a Data Studio dashboard on the exported logs.

Create a custom log-based metric for the specific error to be used in an Alerting Policy.

Grant Project Owner access to the Service Account.

Split the users from business units to multiple projects.

Apply a user- or project-level custom query quota for BigQuery data warehouse.

Create separate copies of your BigQuery data warehouse for each business unit.

Split your BigQuery data warehouse into multiple data warehouses for each business unit.

Change your BigQuery query model from on-demand to flat rate. Apply the appropriate number of slots to each Project.

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

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.

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

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.

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.

Invite the user to transfer their existing account

Invite the user to use an email alias to resolve the conflict

Tell the user that they must delete their existing account

Tell the user to remove all personal email from the existing account

When creating the VM, use machine type n1-standard-96.

When creating the VM, use Intel Skylake as the CPU platform.

Create the VM using Compute Engine default settings. Use gcloud to modify the running instance to have 96 vCPUs.

Start the VM using Compute Engine default settings, and adjust as you go based on Rightsizing Recommendations.

1. Use nslookup to get the IP address for storage.googleapis.com.2. Negotiate with the security team to be able to give a public IP address to the servers.3. Only allow egress traffic from those servers to the IP addresses for storage.googleapis.com.

1. Using Cloud VPN, create a VPN tunnel to a Virtual Private Cloud (VPC) in Google Cloud Platform (GCP).2. In this VPC, create a Compute Engine instance and install the Squid proxy server on this instance.3. Configure your servers to use that instance as a proxy to access Cloud Storage.

1. Use Migrate for Compute Engine (formerly known as Velostrata) to migrate those servers to Compute Engine.2. Create an internal load balancer (ILB) that uses storage.googleapis.com as backend.3. Configure your new instances to use this ILB as proxy.

1. Using Cloud VPN or Interconnect, create a tunnel to a VPC in GCP.2. Use Cloud Router to create a custom route advertisement for 199.36.153.4/30. Announce that network to your on-premises network through the VPN tunnel.3. In your on-premises network, configure your DNS server to resolve *.googleapis.com as a CNAME to restricted.googleapis.com.

Create a Cloud Function to create an instance template.

Create a snapshot schedule for the disk using the desired interval.

Create a cron job to create a new disk from the disk using gcloud.

Create a Cloud Task to create an image and export it to Cloud Storage.

Verify that you are the project billing administrator. Select the associated billing account and create a budget and alert for the appropriate project.

Verify that you are the project billing administrator. Select the associated billing account and create a budget and a custom alert.

Verify that you are the project administrator. Select the associated billing account and create a budget for the appropriate project.

Verify that you are project administrator. Select the associated billing account and create a budget and a custom alert.

Use App Engine and configure Cloud Scheduler to trigger the application using Pub/Sub.

Use Cloud Functions and configure the bucket as a trigger resource.

Use Google Kubernetes Engine and configure a CronJob to trigger the application using Pub/Sub.

Use Dataflow as a batch job, and configure the bucket as a data source.

Use Deployment Manager templates to describe the proposed changes and store them in a Cloud Storage bucket.

Use Deployment Manager templates to describe the proposed changes and store them in Cloud Source Repositories.

Apply the change in a development environment, run gcloud compute instances list, and then save the output in a shared Storage bucket.

Apply the change in a development environment, run gcloud compute instances list, and then save the output in Cloud Source Repositories.

kubectl delete deployment nginx

kubectl delete –deployment=nginx

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

kubectl delete inginx

Use the gcloud pubsub subscriptions seek email-updates command.

Use the gcloud pubsub topics describe new-orders command.

Use the gcloud pubsub subscriptions pull email-updates —auto-ack command.

Use the gcloud pubsub topics list-subscriptions new-orders —1ilter="email-updates" command.

Enable cluster autoscaling on the GKE cluster.

Configure a Vertical Pod Autoscaler on the Deployment.

Configure a Horizontal Pod Autoscaler on the Deployment.

Enable node auto-provisioning on the GKE cluster.

Search for the CMS solution in Google Cloud Marketplace. Use gcloud CLI to deploy the solution.

Search for the CMS solution in Google Cloud Marketplace. Deploy the solution directly from Cloud Marketplace.

Search for the CMS solution in Google Cloud Marketplace. Use Terraform and the Cloud Marketplace ID to deploy the solution with the appropriate parameters.

Use the installation guide of the CMS provider. Perform the installation through your configuration management system.

Use service account credentials in your on-premises application.

Use gcloud to create a key file for the service account that has appropriate permissions.

Set up direct interconnect between your data center and Google Cloud Platform to enable authentication for your on-premises applications.

Go to the IAM & admin console, grant a user account permissions similar to the service account permissions, and use this user account for authentication from your data center.

Add the group for the finance team to roles/billing user role.

Add the group for the finance team to roles/billing admin role.

Add the group for the finance team to roles/billing viewer role.

Add the group for the finance team to roles/billing project/Manager role.

Use Cloud Bigtable for data storage.

Use Cloud SQL for data storage.

Use Cloud Spanner for data storage.

Use Firestore for data storage.

Create a health check on port 443 and use that when creating the Managed Instance Group.

Select Multi-Zone instead of Single-Zone when creating the Managed Instance Group.

In the Instance Template, add the label ‘health-check’.

In the Instance Template, add a startup script that sends a heartbeat to the metadata server.

Set up a budget alert on the project with an amount of 100 dollars, a threshold of 100%, and notification type of “email.”

Set up a budget alert on the billing account with an amount of 100 dollars, a threshold of 100%, and notification type of “email.”

Export the billing data to BigQuery. Create a Cloud Function that uses BigQuery to sum the egress network costs of the exported billing data for the Apache web server for the current month and sends an email if it is over 100 dollars. Schedule the Cloud Function using Cloud Scheduler to run hourly.

Use the Stackdriver Logging Agent to export the Apache web server logs to Stackdriver Logging. Create a Cloud Function that uses BigQuery to parse the HTTP response log data in Stackdriver for the current month and sends an email if the size of all HTTP responses, multiplied by current GCP egress prices, totals over 100 dollars. Schedule the Cloud Function using Cloud Scheduler to run hourly.

Use the Google Cloud Pricing Calculator to determine the cost of every Google Cloud resource you expect to use. Use similar size instances for the web server, and use your current on-premises machines as a comparison for Cloud SQL.

Implement a similar architecture on Google Cloud, and run a reasonable load test on a smaller scale. Check the billing information, and calculate the estimated costs based on the real load your system usually handles.

Use the Google Cloud Pricing Calculator and select the Cloud Operations template to define your web application with as much detail as possible.

Create a Google spreadsheet with multiple Google Cloud resource combinations. On a separate sheet, import the current Google Cloud prices and use these prices for the calculations within formulas.

1. Create a subnetwork in the same VPC, in europe-west1.2. Create the new instance in the new subnetwork and use the first instance's private address as the endpoint.

1. Create a VPC and a subnetwork in europe-west1.2. Expose the application with an internal load balancer.3. Create the new instance in the new subnetwork and use the load balancer's address as the endpoint.

1. Create a subnetwork in the same VPC, in europe-west1.2. Use Cloud VPN to connect the two subnetworks.3. Create the new instance in the new subnetwork and use the first instance's private address as the endpoint.

1. Create a VPC and a subnetwork in europe-west1.2. Peer the 2 VPCs.3. Create the new instance in the new subnetwork and use the first instance's private address as the endpoint.

Use gcloud to expand the IP range of the current subnet.

Delete the subnet, and recreate it using a wider range of IP addresses.

Create a new project. Use Shared VPC to share the current network with the new project.

Create a new subnet with the same starting IP but a wider range to overwrite the current subnet.

Create a Service Account in your own project, and grant this Service Account access to BigGuery in your project

Create a Service Account in your own project, and ask the partner to grant this Service Account access to BigQuery in their project

Ask the partner to create a Service Account in their project, and have them give the Service Account access to BigQuery in their project

Ask the partner to create a Service Account in their project, and grant their Service Account access to the BigQuery dataset in your project

Create a Cloud Storage bucket. Establish an Identity and Access Management (IAM) role with write permissions to the bucket. Use the gsutil tool to directly copy files over the network to Cloud Storage.

Set up a Cloud Interconnect connection between the on-premises network and Google Cloud. Establish a private endpoint for Filestore access. Transfer the data from the existing Network File System (NFS) to Filestore.

Use Transfer Appliance to request an appliance. Load the data locally, and ship the appliance back to Google for ingestion into Cloud Storage.

Use Storage Transfer Service to move the data from the selected on-premises file storage systems to a Cloud Storage bucket.

Create one CNAME record to point mydomain.com to the load balancer, and create two A records to point WWW and HOME lo mydomain.com respectively.

Create one CNAME record to point mydomain.com to the load balancer, and create two AAAA records to point WWW and HOME to mydomain.com respectively.

Create one A record to point mydomain.com to the load balancer, and create two CNAME records to point WWW and HOME to mydomain.com respectively.

Create one A record to point mydomain.com lo the load balancer, and create two NS records to point WWW and HOME to mydomain.com respectively.

1. Create a Cloud Function that uses a Cloud Pub/Sub trigger on that topic.2. Call your application on Cloud Run from the Cloud Function for every message.

1. Grant the Pub/Sub Subscriber role to the service account used by Cloud Run.2. Create a Cloud Pub/Sub subscription for that topic.3. Make your application pull messages from that subscription.

1. Create a service account.2. Give the Cloud Run Invoker role to that service account for your Cloud Run application.3. Create a Cloud Pub/Sub subscription that uses that service account and uses your Cloud Run application as the push endpoint.

1. Deploy your application on Cloud Run on GKE with the connectivity set to Internal.2. Create a Cloud Pub/Sub subscription for that topic.3. In the same Google Kubernetes Engine cluster as your application, deploy a container that takes the messages and sends them to your application.

Sync Identities with Cloud Directory Sync, and then enable SAML for single sign-on

Sync Identities in the Google Admin console, and then enable Oauth for single sign-on

Sync identities with 3rd party LDAP sync, and then copy passwords to allow simplified login with (he same credentials

Sync identities with Cloud Directory Sync, and then copy passwords to allow simplified login with the same credentials.

Deploy the application lo Cloud. Run. Use gradual rollouts for traffic splitting .

Deploy the application lo Google Kubemetes Engine. Use Anthos Service Mesh for traffic splitting.

Deploy the application to Cloud functions. Saucily the version number in the functions name.

Deploy the application to App Engine. For each new version, create a new service.

Create a Billing account, associate a payment method with it, and provide all project creators with permission to associate that billing account with their projects.

Grant all engineer’s permission to create their own billing accounts for each new project.

Apply for monthly invoiced billing, and have a single invoice tor the project paid by the finance team.

Create a billing account, associate it with a monthly purchase order (PO), and send the PO to Google Cloud.

Create an instance template. Set the disk type to be an SSD Persistent Disk. Launch the instance template as part of a stateful managed instance group.

Create an instance template. Set the disk type to be an SSD Persistent Disk. Launch the instance template as part of a stateless managed instance group.

Create an instance template. Set the disk type to be Hyperdisk Extreme. Launch the instance template as part of a stateful managed instance group.

Create an instance template. Set the disk type to be Hyperdisk Extreme. Launch the instance template as part of a stateless managed instance group.

Migrate from MySQL to Cloud SQL, from Kafka to Memorystore, and from Cloud SQL for PostgreSQL to Cloud SQL

Migrate from MySQL to Cloud Spanner, from Kafka to Memorystore, and from Cloud SOL for PostgreSQL to Cloud SQL

Migrate from MySQL to Cloud SOL, from Kafka to Pub/Sub, and from Cloud SOL for PostgreSQL to BigQuery.

Migrate from MySQL to Cloud Spanner, from Kafka to Pub/Sub. and from Cloud SQL for PostgreSQL to BigQuery

Create two configurations using gcloud config. Write a script that sets configurations as active, individually. For each configuration, use gcloud compute instances list to get a list of compute resources.

Create two configurations using gsutil config. Write a script that sets configurations as active, individually. For each configuration, use gsutil compute instances list to get a list of compute resources.

Go to Cloud Shell and export this information to Cloud Storage on a daily basis.

Go to GCP Console and export this information to Cloud SQL on a daily basis.

Use gcloud container clusters upgrade. Deploy the new services.

Create a new Node Pool and specify machine type n2–highmem–16. Deploy the new pods.

Create a new cluster with n2–highmem–16 nodes. Redeploy the pods and delete the old cluster.

Create a new cluster with both n1–standard–2 and n2–highmem–16 nodes. Redeploy the pods and delete the old cluster.

Use permissions in your role that use the ‘supported’ support level for role permissions. Set the role stage to ALPHA while testing the role permissions.

Use permissions in your role that use the ‘supported’ support level for role permissions. Set the role stage to BETA while testing the role permissions.

Use permissions in your role that use the ‘testing’ support level for role permissions. Set the role stage to ALPHA while testing the role permissions.

Use permissions in your role that use the ‘testing’ support level for role permissions. Set the role stage to BETA while testing the role permissions.

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

Enable parallel composite uploads using gsutil on the file transfer.

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

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

Configure your GKE cluster with one node, and set the node to have a static external IP address. Ensure that the GKE cluster autoscaler is off. Send the external IP address of the node to the vendor to be added to the allowlist.

Configure your GKE cluster with private nodes. Configure a Cloud NAT instance with static IP addresses. Provide these IP addresses to the vendor to be added to the allowlist.

Configure your GKE cluster with public nodes. Write a Cloud Function that pulls the public IP addresses of each node in the cluster. Trigger the function to run every day with Cloud Scheduler. Send the list to the vendor by email every day.

Configure your GKE cluster with private nodes. Configure a Cloud NAT instance with dynamic IP addresses. Provide these IP addresses to the vendor to be added to the allowlist.

Enable retention policies on the bucket, and use Cloud Scheduler to invoke a Cloud Function to move or delete your documents based on their metadata.

Enable retention policies on the bucket, use lifecycle rules to change the storage classes of the objects, set the number of versions, and delete old files.

Enable object versioning on the bucket, and use Cloud Scheduler to invoke a Cloud Functions instance to move or delete your documents based on their metadata.

Enable object versioning on the bucket, use lifecycle conditions to change the storage class of the objects, set the number of versions, and delete old files.

Create an alert in Cloud Monitoring to alert when the percentage of high priority CPU utilization reaches 45% If you exceed this threshold, add nodes lo your instance.

Create an alert in Cloud Monitoring to alert when the percentage to high priority CPU utilization reaches 45% Use database query statistics to identify queries that result in high CPU usage, and then rewrite those queries to optimize their resource usage

Create an alert in Cloud Monitoring to alert when the percentage of high priority CPU utilization reaches 65% If you exceed this threshold, add nodes to your instance

Create an alert in Cloud Monitoring to alert when the percentage of high priority CPU utilization reaches 65%. Use database query statistics to identity queries that result in high CPU usage, and then rewrite those queries to optimize their resource usage.

Enable OS Login for all VMs. Use IAM roles to grant user permissions.

Enable OS Login for all VMs. Write custom startup scripts to update user permissions.

Require your developers to create public SSH keys. Make the owner of the public key the root user.

Require your developers to create public SSH keys. Write custom startup scripts to update user permissions.