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

Meet with the cloud enablement team to discuss load balancer options.

Redesign the application to use a distributed user session service that does not rely on WebSockets and HTTP sessions.

Review the encryption requirements for WebSocket connections with the security team.

Convert the WebSocket code to use HTTP streaming.

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.

Upload the data to BigQuery using the bq command line tool.

Upload the data to Cloud Storage using the gsutil command line tool.

Upload the data into Cloud SQL using the import function in the console.

Upload the data into Cloud Spanner using the import function in the console.

In Cloud Identity, set up SSO with Google as an identity provider to access custom SAML apps.

In Cloud Identity, set up SSO with a third-party identity provider with Google as a service provider.

Obtain OAuth 2.0 credentials, configure the user consent screen, and set up OAuth 2.0 for Mobile & Desktop Apps.

Obtain OAuth 2.0 credentials, configure the user consent screen, and set up OAuth 2.0 for Web Server Applications.

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.

1. Build an instruction guide to install Cassandra on GCP.2. Make the instruction guide accessible to your developers.

1. Advise your developers to go to Cloud Marketplace.2. Ask the developers to launch a Cassandra image for their development work.

1. Build a Cassandra Compute Engine instance and take a snapshot of it.2. Use the snapshot to create instances for your developers.

1. Build a Cassandra Compute Engine instance and take a snapshot of it.2.Upload the snapshot to Cloud Storage and make it accessible to your developers.3.Build instructions to create a Compute Engine instance from the snapshot so that developers can do it themselves.

Add the auditors group to the ‘logging.viewer’ and ‘bigQuery.dataViewer’ predefined IAM roles.

Add the auditors group to two new custom IAM roles.

Add the auditor user accounts to the ‘logging.viewer’ and ‘bigQuery.dataViewer’ predefined IAM roles.

Add the auditor user accounts to two new custom IAM roles.

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.

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.

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.

Google Compute Engine

Google App Engine

Cloud Functions

Google Kubernetes Engine

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

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.

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.

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.

Use gcloud to create the new project, and then deploy your application to the new project.

Use gcloud to create the new project and to copy the deployed application to the new project.

Create a Deployment Manager configuration file that copies the current App Engine deployment into a new project.

Deploy your application again using gcloud and specify the project parameter with the new project name to create the new project.

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.

Use kubectl app deploy .

Use gcloud app deploy .

Create a docker image from the Dockerfile and upload it to Container Registry. Create a Deployment YAML file to point to that image. Use kubectl to create the deployment with that file.

Create a docker image from the Dockerfile and upload it to Cloud Storage. Create a Deployment YAML file to point to that image. Use kubectl to create the deployment with that file.

Create a single budget for all projects and configure budget alerts on this budget.

Create a separate billing account per sandbox project and enable BigQuery billing exports. Create a Data Studio dashboard to plot the spending per billing account.

Create a budget per project and configure budget alerts on all of these budgets.

Create a single billing account for all sandbox projects and enable BigQuery billing exports. Create a Data Studio dashboard to plot the spending per project.

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.

Create an export to the sink that saves logs from Cloud Audit to BigQuery.

Create an export to the sink that saves logs from Cloud Audit to a Coldline Storage bucket.

Write a custom script that uses logging API to copy the logs from Stackdriver logs to BigQuery.

Export these logs to Cloud Pub/Sub and write a Cloud Dataflow pipeline to store logs to Cloud SQL.

Use gcloud iam roles copy and specify the production project as the destination project.

Use gcloud iam roles copy and specify your organization as the destination organization.

In the Google Cloud Platform Console, use the ‘create role from role’ functionality.

In the Google Cloud Platform Console, use the ‘create role’ functionality and select all applicable permissions.

Create a Cloud Memorystore for Redis instance with 32-GB capacity.

Run it on Compute Engine, and choose a custom instance type with 6 vCPUs and 32 GB of memory.

Package it in a container image, and run it on Kubernetes Engine, using n1-standard-32 instances as nodes.

Run it on Compute Engine, choose the instance type n1-standard-1, and add an SSD persistent disk of 32 GB.

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.

Assign the appropriate permissions, and then use Cloud Monitoring to review metrics

Use the export logs API to provide the Admin Activity Audit Logs in the format they want

Turn on Data Access Logs for the buckets they want to audit, and Then build a query in the log viewer that filters on Cloud Storage

Assign the appropriate permissions, and then create a Data Studio report on Admin Activity Audit Logs

Create a file in Cloud Storage per device and append new data to that file.

Create a file in Cloud Filestore per device and append new data to that file.

Ingest the data into Datastore. Store data in an entity group based on the device.

Ingest the data into Cloud Bigtable. Create a row key based on the event timestamp.

kubectl get deployments –output=pods

gcloud get pods –selector=”app=prod”

kubectl get pods -I “app=prod”

gcloud list gke-deployments -filter={pod }

1. Create an ingress firewall rule with the following settings:• Targets: all instances• Source filter: IP ranges (with the range set to 10.0.2.0/24)• Protocols: allow all2. Create an ingress firewall rule with the following settings:• Targets: all instances• Source filter: IP ranges (with the range set to 10.0.1.0/24)• Protocols: allow all

1. Create an ingress firewall rule with the following settings:• Targets: all instances with tier #2 service account• Source filter: all instances with tier #1 service account• Protocols: allow TCP:80802. Create an ingress firewall rule with the following settings:• Targets: all instances with tier #3 service account• Source filter: all instances with tier #2 service account• Protocols: allow TCP: 8080

1. Create an ingress firewall rule with the following settings:• Targets: all instances with tier #2 service account• Source filter: all instances with tier #1 service account• Protocols: allow all2. Create an ingress firewall rule with the following settings:• Targets: all instances with tier #3 service account• Source filter: all instances with tier #2 service account• Protocols: allow all

1. Create an egress firewall rule with the following settings:• Targets: all instances• Source filter: IP ranges (with the range set to 10.0.2.0/24)• Protocols: allow TCP: 80802. Create an egress firewall rule with the following settings:• Targets: all instances• Source filter: IP ranges (with the range set to 10.0.1.0/24)• Protocols: allow TCP: 8080

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.

Link the acquired company’s projects to your company's billing account.

Configure the acquired company's billing account and your company's billing account to export the billing data into the same BigQuery dataset.

Migrate the acquired company’s projects into your company’s GCP organization. Link the migrated projects to your company's billing account.

Create a new GCP organization and a new billing account. Migrate the acquired company's projects and your company's projects into the new GCP organization and link the projects to the new billing account.

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:///*

Fill in local SSD. Fill in persistent disk storage and snapshot storage.

Fill in local SSD. Add estimated cost for cluster management.

Select Add GPUs. Fill in persistent disk storage and snapshot storage.

Select Add GPUs. Add estimated cost for cluster management.

Run gcloud configurations list followed by gcloud configurations activate .

Run gcloud config list followed by gcloud config activate.

Run gcloud config configurations list followed by gcloud config configurations activate.

Re-authenticate with the gcloud auth login command and select the correct configurations on login.

Use Bigtable.

Use BigQuery.

Use Spanner.

Use Cloud SQL for PostgreSQL.

Deployment Manager

Cloud Composer

Managed Instance Group

Unmanaged Instance Group

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.

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.

Run a test using simulated maintenance events. If the test is successful, use preemptible N1 Standard VMs when running future jobs.

Run a test using simulated maintenance events. If the test is successful, use N1 Standard VMs when running future jobs.

Run a test using a managed instance group. If the test is successful, use N1 Standard VMs in the managed instance group when running future jobs.

Run a test using N1 standard VMs instead of N2. If the test is successful, use N1 Standard VMs when running future jobs.

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.

Use the projects. move method to move the project to your organization. Update the billing account of the project to that of your organization.

Ensure that you have an Organization Administrator Identity and Access Management (IAM) role assigned to you in both organizations. Navigate to the Resource Manager in the startup's Google Cloud organization, and drag the project to your company's organization.

Create a Private Catalog tor the Google Cloud Marketplace, and upload the resources of the startup’s production project to the Catalog. Share the Catalog with your organization, and deploy the resources in your company’s project.

Create an infrastructure-as-code template tor all resources in the project by using Terraform. and deploy that template to a new project in your organization. Delete the protect from the startup's Google Cloud organization.

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.

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

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

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.

• Ensure that the Ops Agent Is Installed on the Compute Engine instance.• Create a custom metric in the Cloud Monitoring dashboard.• Provide the security team member with access to this dashboard.

• Ensure that the Ops Agent is installed on tie Compute Engine instance.• Provide the security team member roles/configure.inventoryViewer permission.

• Ensure that the OS Config agent Is Installed on the Compute Engine instance.• Provide the security team member roles/configure.vulnerabilityViewer permission.

• Ensure that the OS Config agent is installed on the Compute Engine instance• Create a log sink Co a BigQuery dataset.• Provide the security team member with access to this dataset.

.00.0.0/0

10.0.0.0/8

172.16.0.0/12

192.168.0.0/16

Upload the code to Cloud Functions. Use Cloud Scheduler to start the application.

Create a container for the set of binaries. Use Cloud Scheduler to start a Cloud Run job for the container.

Create a container for the set of binaries Deploy the container to Google Kubernetes Engine (GKE) and use the Kubernetes scheduler to start the application.

Lift and shift to a VM on Compute Engine. Use an instance schedule to start and stop the instance.

In the console, validate which SSH keys have been stored as project-wide keys.

Navigate to Identity-Aware Proxy and check the permissions for these resources.

Enable Audit Logs on the IAM & admin page for all resources, and validate the results.

Use the command gcloud projects get–iam–policy to view the current role assignments.

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.

Deploy a new version of your application in Google Kubernetes Engine instead of App Engine and then use GCP Console to split traffic.

Deploy a new version of your application in a Compute Engine instance instead of App Engine and then use GCP Console to split traffic.

Deploy a new version as a separate app in App Engine. Then configure App Engine using GCP Console to split traffic between the two apps.

Deploy a new version of your application in App Engine. Then go to App Engine settings in GCP Console and split traffic between the current version and newly deployed versions accordingly.

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.

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.

Configure an SSL Proxy load balancer in front of the application servers.

Configure an Internal UDP load balancer in front of the application servers.

Configure an External HTTP(s) load balancer in front of the application servers.

Configure an External Network load balancer in front of the application servers.

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.

Download and deploy the Jenkins Java WAR to App Engine Standard.

Create a new Compute Engine instance and install Jenkins through the command line interface.

Create a Kubernetes cluster on Compute Engine and create a deployment with the Jenkins Docker image.

Use GCP Marketplace to launch the Jenkins solution.

In the project where the images are stored, grant the Storage Object Viewer IAM role to the service account used by the Kubernetes nodes.

When you create the GKE cluster, choose the Allow full access to all Cloud APIs option under ‘Access scopes’.

Create a service account, and give it access to Cloud Storage. Create a P12 key for this service account and use it as an imagePullSecrets in Kubernetes.

Configure the ACLs on each image in Cloud Storage to give read-only access to the default Compute Engine service account.

Implement a Cloud Run job to rotate all service account keys periodically in pj-sa. Enforce an org policy to deny service account key creation with an exception to pj-sa.

Implement a Kubernetes Cronjob to rotate all service account keys periodically. Disable attachment ofservice accounts to resources in all projects with an exception to pj-sa.

Enforce an org policy constraint allowing the lifetime of service account keys to be 24 hours. Enforce an org policy constraint denying service account key creation with an exception on pj-sa.

Enforce a DENY org policy constraint over the lifetime of service account keys for 24 hours. Disable attachment of service accounts to resources in all projects with an exception to pj-sa.

Ask the auditor for their Google account, and give them the Viewer role on the project.

Ask the auditor for their Google account, and give them the Security Reviewer role on the project.

Create a temporary account for the auditor in Cloud Identity, and give that account the Viewer role on the project.

Create a temporary account for the auditor in Cloud Identity, and give that account the Security Reviewer role on the project.

Contact cloud-billing@google.com with your bank account details and request a corporate billing account for your company.

Create a ticket with Google Support and wait for their call to share your credit card details over the phone.

In the Google Platform Console, go to the Resource Manage and move all projects to the root Organization.

In the Google Cloud Platform Console, create a new billing account and set up a payment method.

Run a Kubernetes job to scan the bucket regularly for incoming files, and call the Speech-to-Text API for each unprocessed file.

Create an App Engine standard environment triggered by Cloud Storage bucket events to submit the file URI to the Google Speech-to-Text API.

Run a Python script by using a Linux cron job in Compute Engine to scan the bucket regularly for incoming files, and call the Speech-to-Text API for each unprocessed file.

Create a Cloud Function triggered by Cloud Storage bucket events to submit the file URI to the Google Speech-to-Text API.

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 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.

Create a custom role, and add all the required compute.disks.list and compute, images.list permissions as includedPermissions. Grant the custom role to the user at the project level.

Create a custom role based on the Compute Image User role Add the compute.disks, list to theincludedPermissions field Grant the custom role to the user at the project level

Grant the Compute Storage Admin role at the project level.

Create a custom role based on the Compute Storage Admin role. Exclude unnecessary permissions from the custom role. Grant the custom role to the user at the project level.

Save the incoming votes to Firestore. Use Cloud Scheduler to trigger a Cloud Functions instance to periodically process the votes.

Use a dedicated instance to process the incoming votes. Send the votes directly to this instance.

Save the incoming votes to a JSON file on Cloud Storage. Process the votes in a batch at the end of the day.

Save the incoming votes to Pub/Sub. Use the Pub/Sub topic to trigger a Cloud Functions instance to process the votes.

Use Cloud Logging filters to create log-based metrics for firewall and instance actions. Monitor the changes and set up reasonable alerts.

Install Kibana on a compute Instance. Create a log sink to forward Cloud Audit Logs filtered for firewalls andcompute instances to Pub/Sub. Target the Pub/Sub topic to push messages to the Kibana instance. Analyze the logs on Kibana in real time.

Turn on Google Cloud firewall rules logging, and set up alerts for any insert, update, or delete events.

Create a log sink to forward Cloud Audit Logs filtered for firewalls and compute instances to Cloud Storage.Use BigQuery to periodically analyze log events in the storage bucket.

Run gcloud auth login, enter your login credentials in the dialog window, and paste the received login token to gcloud CLI.

Create a Google Cloud service account, and download the service account key. Place the key file in a folder on your machine where gcloud CLI can find it.

Download your Cloud Identity user account key. Place the key file in a folder on your machine where gcloud CLI can find it.

Run gcloud config set compute/zone $my_zone to set the default zone for gcloud CLI.

Run gcloud config set project $my_project to set the default project for gcloud CLI.

Export your bill to a Cloud Storage bucket, and then import into Cloud Bigtable for analysis.

Export your bill to a Cloud Storage bucket, and then import into Google Sheets for analysis.

Export your transactions to a local file, and perform analysis with a desktop tool.

Export your bill to a BigQuery dataset, and then write time window-based SQL queries for analysis.

Grant each individual external consultant the role of BigQuery Editor

Grant each individual external consultant the role of BigQuery Viewer

Create a Google Group that contains the consultants and grant the group the role of BigQuery Editor

Create a Google Group that contains the consultants, and grant the group the role of BigQuery Viewer

Ensure the checkbox "Serve this revision immediately" is unchecked when deploying the new revision. Before changing the traffic rules, use a traffic simulation tool to send load to the new revision.

Configure service autoscaling and set the minimum number of instances to 2.

Configure revision autoscaling for the new revision and set the minimum number of instances to 2.

Configure revision autoscaling for the existing revision and set the minimum number of instances to 2.

Create a bash script that contains all requirement steps as gcloud commands

Develop templates for the environment using Cloud Deployment Manager

Use curl in a terminal to send a REST request to the relevant Google API for each individual resource.

Use the Cloud Console interface to provision and manage all related resources

Deploy your application to a second Cloud Run service, and ask your customers to use the second Cloud Run service.

Ask your customers to retry access to your service with exponential backoff to mitigate any potential problems after the new revision is deployed.

Gradually roll out the new revision and split customer traffic between the revisions to allow rollback in case a problem occurs.

Send all customer traffic to the new revision, and roll back to a previous revision if you witness any problems in production.

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.

Project Editor

Storage Admin

Storage Object Admin

Storage Object Creator

Add your SREs to roles/iam.roleAdmin role.

Add your SREs to roles/accessapproval approver role.

Add your SREs to a group and then add this group to roles/iam roleAdmin role.

Add your SREs to a group and then add this group to roles/accessapproval approver role.

Create an Instance template with the container image, and deploy a Managed Instance Group withAutoscaling.

Upload Docker images to Artifact Registry, and deploy the application on Google Kubernetes Engine usingStandard mode.

Upload Docker images to the Cloud Storage, and deploy the application on Google Kubernetes Engine usingStandard mode.

Upload Docker images to Artifact Registry, and deploy the application on Cloud Run.

Create a multi-region Cloud Spanner instance with an optimized schema.

Create a multi-region Firestore database with aggregation query enabled.

Create a multi-region Cloud SQL for PostgreSQL database with optimized indexes.

Create a multi-region BigQuery dataset with optimized tables.

Set metadata to enable-oslogin=true for the instance. Grant the dev1 group the compute.osLogin role. Direct them to use the Cloud Shell to ssh to that instance.

Set metadata to enable-oslogin=true for the instance. Set the service account to no service account for that instance. Direct them to use the Cloud Shell to ssh to that instance.

Enable block project wide keys for the instance. Generate an SSH key for each user in the dev1 group. Distribute the keys to dev1 users and direct them to use their third-party tools to connect.

Enable block project wide keys for the instance. Generate an SSH key and associate the key with that instance. Distribute the key to dev1 users and direct them to use their third-party tools to connect.

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.

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-purposemachine 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

Run gcloud iam roles describe roles/spanner.databaseUser. Add the users to the role.

Run gcloud iam roles describe roles/spanner.databaseUser. Add the users to a new group. Add the group to the role.

Run gcloud iam roles describe roles/spanner.viewer --project my-project. Add the users to the role.

Run gcloud iam roles describe roles/spanner.viewer --project my-project. Add the users to a new group. Add the group to the role.

• 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.

Migrate the web application to App Engine and the backend API to Cloud Run Use Cloud Tasks to run your background job on Compute Engine

Migrate the web application to App Engine and the backend API to Cloud Run. Use Cloud Tasks to run your background job on Cloud Run.

Run the web application on a Cloud Storage bucket and the backend API on Cloud Run Use Cloud Tasks to run your background job on Cloud Run.

Run the web application on a Cloud Storage bucket and the backend API on Cloud Run. Use Cloud Tasks to run your background job on Compute Engine

Set the maximum number of instances to 1.

Decrease the maximum number of instances to 3.

Use a TCP health check instead of an HTTP health check.

Increase the initial delay of the HTTP health check to 200 seconds.

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.

Perform a rolling-action start-update with maxSurge set to 0 and maxUnavailable set to 1.

Perform a rolling-action start-update with maxSurge set to 1 and maxUnavailable set to 0.

Create a new managed instance group with an updated instance template. Add the group to the backend service for the load balancer. When all instances in the new managed instance group are healthy, delete the old managed instance group.

Create a new instance template with the new application version. Update the existing managed instance group with the new instance template. Delete the instances in the managed instance group to allow the managed instance group to recreate the instance using the new instance template.

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.

Run gcloud compute instances list to get the IP address of the instance, then use the ssh command.

Use the gcloud compute ssh command.

Create a key with the ssh-keygen command. Then use the gcloud compute ssh command.

Create a key with the ssh-keygen command. Upload the key to the instance. Run gcloud compute instances list to get the IP address of the instance, then use the ssh command.

Create the GKE cluster and deploy the application. Request a security exception to create a Google service account key. Set the constraints/iam.serviceAccountKeyExpiryHours organization policy to 8 hours.

Create the GKE cluster and deploy the application. Request a security exception to create a Google service account key. Set the constraints/iam.serviceAccountKeyExpiryHours organization policy to 24 hours.

Create the GKE cluster with Workload Identity Federation. Configure the default node service account to access the bucket. Deploy the application into the cluster so the application can use the node service account permissions. Use Identity and Access Management (IAM) to grant the service account access to the bucket.

Create the GKE cluster with Workload Identity Federation. Create a Google service account and a Kubernetes ServiceAccount, and configure both service accounts to use Workload Identity Federation. Attach the Kubernetes ServiceAccount to the application Pods and configure the Google service account to access the bucket with Identity and Access Management (IAM).

Create a VPC with non-overlapping CIDR ranges compared to your on-premises network. When migrating individual workloads, assign each workload a new static internal IP address.

Migrate your DNS server first. Configure Cloud DNS with a forwarding zone to your migrated DNS server. Then migrate all other workloads with ephemeral internal IP addresses.

Migrate all workloads to a single VPC subnet. Configure Cloud NAT for the subnet and manually assign a static IP address to the Cloud NAT gateway.

Create a VPC with the same CIDR ranges as your on-premises network. When migrating individual workloads, assign each workload the same static internal IP address.

Enable the Identity Aware Proxy API on the project.

Scan the bucker using the Data Loss Prevention API.

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

Enable Data Access audit logs for the Cloud Storage API.

Use the command gcloud auth login and point it to the private key

Use the command gcloud auth activate-service-account and point it to the private key

Place the private key file in the installation directory of the Cloud SDK and rename it to "credentials ison"

Place the private key file in your home directory and rename it to ‘’GOOGLE_APPUCATION_CREDENTiALS".

Navigate to Cloud Logging and view the application logs.

Connect to the instance’s serial console and read the application logs.

Configure a Health Check on the instance and set a Low Healthy Threshold value.

Install and configure the Cloud Logging Agent and view the logs from Cloud Logging.

Modify the minimum number of Cloud Run instances.

Set a minimum concurrent requests environment variable for the application.

Modify the maximum number of Cloud Run instances.

Use traffic splitting.

Configure username and password by using gcloud configure set proxy/username and gcloud configure set proxy/ proxy/password commands.

Encode username and password in sha256 encoding, and save it to a text file. Use filename as a value in the gcloud configure set core/custom_ca_certs_file command.

Provide values for CLOUDSDK_USERNAME and CLOUDSDK_PASSWORD in the gcloud CLI tool configure file.

Set the CLOUDSDK_PROXY_USERNAME and CLOUDSDK_PROXY PASSWORD properties by using environment variables in your command line tool.

Filter the Activity log to view the Configuration category. Filter the Resource type to Service Account.

Filter the Activity log to view the Configuration category. Filter the Resource type to Google Project.

Filter the Activity log to view the Data Access category. Filter the Resource type to Service Account.

Filter the Activity log to view the Data Access category. Filter the Resource type to Google Project.

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.

Use gcloud storage for the video files. Dataflow for the data warehouse data, and Storage Transfer Service for the PNG files.

Use Transfer Appliance for the videos. BigQuery Data Transfer Service for the data warehouse data, and Storage Transfer Service for the PNG files.

Use Storage Transfer Service for the video files, BigQuery Data Transfer Service for the data warehouse data, and Storage Transfer Service for the PNG files.

Use Cloud Data Fusion for the video files, Dataflow for the data warehouse data, and Storage Transfer Service for the PNG files.