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✋ Join a live online community and a course taught by industry experts and pass the GCP Associate Cloud Engineer (ACE) confidently. We aim to build an ecosystem of Information Technology (IT) certifications and online courses in cooperation with the technology industry. We believe it will give our students 100% confidence in the pacing market in an open-source environment. We are just at the beginning of our way, so it's even better for you to join now!

1. Always happy to answer your questions on Udemy's Q&A's and outside :)
2. Failed? Please submit a screenshot of your exam result and request a refund (via our upcoming platform, not possible on Udemy); we'll always accept it.
3. Learn about topics, such as:
   • BigQuery;
   • Billing Administrator;
   • Cloud Audit;
   • Cloud Bigtable;
   • Cloud Concepts;
   • Cloud Dataflow;
   • Cloud Datastore;
   • Cloud Identity and Access Management (Cloud IAM);
   • Cloud Logging;
   • Cloud Pub/Sub;
   • Cloud Run;
   • Cloud SDK;
   • Cloud Shell;
   • Cloud Spanner;
   • Cloud SQL;
   • Cloud Storage;
   • Coldline Storage;
   • Compute Engine;
   • Deployment Manager;
   • Google Cloud Platform Console (GCP Console);
   • Google App Engine;
   • Google Cloud Marketplace;
   • Google Kubernetes Engine (GKE);
   • Nearline Storage;
   • Project Billing Manager;
   • Stackdriver;
   • Virtual Private Cloud (VPC);
   • Much More!
4. Questions are similar to the actual exam, without duplications (like in other courses ;-)).
5. The Practice Tests Exams simulate the actual exam's content, timing, and percentage required to pass the exam.
6. This course is not GCP Associate Cloud Engineer (ACE) Exam Dump. Some people use brain dumps or exam dumps, but that's absurd, which we don't practice.
7. 179 unique questions.

v1.0.0: May 25, 2023.

• Launch of the course.

v1.1.0: June 9, 2023.

• Proofreading & deleted 1 repeated question.

v1.1.0: August 5, 2024.

• AI-generated explanations (only paid Udemy).

We are so thankful for every contribution, which makes sure we can deliver top-notch content. Whenever you find a missing resource, broken link in a Table of Contents, the wrong answer, please submit an issue. Even better would be a Pull Request (PR).

• 👨‍🎓 Students preparing for the Google Cloud Platform (GCP) Associate Cloud Engineer (ACE) Exam;
• 👨‍🎓 AWS Engineers;
• 👨‍🎓 Azure Engineers;
• 👨‍🎓 Cloud Architects;
• 👨‍🎓 Cloud Engineers;
• 👨‍🎓 DevOps Engineers;
• 👨‍🎓 Enterprise Architects;
• 👨‍🎓 Google Cloud Platform (GCP) Engineers;
• 👨‍🎓 Infrastructure Engineers;
• 👨‍🎓 IT Professionals;
• 👨‍🎓 Lead Engineers;
• 👨‍🎓 Product Architects;
• 👨‍🎓 Product Managers;
• 👨‍🎓 Product Owners;
• 👨‍🎓 Project Managers;
• 👨‍🎓 Scrum Masters;
• 👨‍🎓 Security Engineers;
• 👨‍🎓 Site Reliability Engineers;
• 👨‍🎓 Software Developers/Engineers;
• 👨‍🎓 Software Testers;
• 👨‍🎓 Solution Architects;
• 👨‍🎓 Team Leaders.

• 🤩 Excitement to learn!
• 0️⃣ Prior knowledge is required;
• ✅ You can pass the Google Cloud Platform (GCP) Associate Cloud Engineer (ACE) Exam solely based on our Practice Tests Exams.

• Use granular logging statements within a Deployment Manager template authored in Python.
• Monitor activity of the Deployment Manager execution on the Stackdriver Logging page of the GCP Console.
• Execute the Deployment Manager template against a separate project with the same configuration, and monitor for failures.
• Execute the Deployment Manager template using the C-preview option in the same project, and observe the state of interdependent resources.

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• 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 all. 2. 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: 8080. 2. 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 all. 2. 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: 8080. 2. 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.

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• 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.

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• Enable the Cloud Pub/Sub API in the API Library on the GCP Console.
• Rely on the automatic enablement of the Cloud Pub/Sub API when the Service Account accesses it.
• Use Deployment Manager to deploy your application. Rely on the automatic enablement of all APIs used by the application being deployed.
• Grant the App Engine Default service account the role of Cloud Pub/Sub Admin. Have your application enable the API on the first connection to Cloud Pub/Sub.

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• 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.

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• 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.

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• 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.

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• 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.

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• Create a Google Kubernetes Engine cluster, and use horizontal pod autoscaling to scale the application.
• Create an instance template, and use the template in a Managed Instance Group with autoscaling configured.
• Create an instance template, and use the template in a Managed Instance Group that scales up and down based on the time of day.
• Use a set of third-party tools to build automation around scaling the application up and down, based on Stackdriver CPU usage monitoring.

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• Create a service account with an access scope. Use the access scope 'https://www.googleapis.com/auth/devstorage.write_only'.
• Create a service account with an access scope. Use the access scope 'https://www.googleapis.com/auth/cloud-platform'.
• Create a service account and add it to the IAM role 'storage.objectCreator' for that bucket.
• Create a service account and add it to the IAM role 'storage.objectAdmin' for that bucket.

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• Create a signed URL with a four-hour expiration and share the URL with the company.
• Set object access to 'public' and use object lifecycle management to remove the object after four hours.
• Configure the storage bucket as a static website and furnish the object's URL to the company. Delete the object from the storage bucket after four hours.
• Create a new Cloud Storage bucket specifically for the external company to access. Copy the object to that bucket. Delete the bucket after four hours have passed.

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• 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.

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

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• Create a single custom VPC with 2 subnets. Create each subnet in a different region and with a different CIDR range.
• Create a single custom VPC with 2 subnets. Create each subnet in the same region and with the same CIDR range.
• Create 2 custom VPCs, each with a single subnet. Create each subnet is a different region and with a different CIDR range.
• Create 2 custom VPCs, each with a single subnet. Create each subnet in the same region and with the same CIDR range.

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• 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.

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• 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.

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• 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.

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• Verify that you are Project Billing Manager for the GCP project. Update the existing project to link it to the existing billing account.
• Verify that you are Project Billing Manager for the GCP project. Create a new billing account and link the new billing account to the existing project.
• Verify that you are Billing Administrator for the billing account. Create a new project and link the new project to the existing billing account.
• Verify that you are Billing Administrator for the billing account. Update the existing project to link it to the existing billing account.

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• Using the GCP Console, filter the Activity log to view the information.
• Using the GCP Console, filter the Stackdriver log to view the information.
• View the bucket in the Storage section of the GCP Console.
• Create a trace in Stackdriver to view the information.

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• Arrange to switch to Flat-Rate pricing for this query, then move back to on-demand.
• Use the command line to run a dry run query to estimate the number of bytes read. Then convert that bytes estimate to dollars using the Pricing Calculator.
• Use the command line to run a dry run query to estimate the number of bytes returned. Then convert that bytes estimate to dollars using the Pricing Calculator.
• Run a select count (*) to get an idea of how many records your query will look through. Then convert that number of rows to dollars using the Pricing Calculator.

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• 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.

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• Deployment Manager.
• Cloud Composer.
• Managed Instance Group.
• Unmanaged Instance Group.

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• Install a RDP client on your desktop. Verify that a firewall rule for port 3389 exists.
• Install a RDP client in your desktop. Set a Windows username and password in the GCP Console. Use the credentials to log in to the instance.
• Set a Windows password in the GCP Console. Verify that a firewall rule for port 22 exists. Click the RDP button in the GCP Console and supply the credentials to log in.
• Set a Windows username and password in the GCP Console. Verify that a firewall rule for port 3389 exists. Click the RDP button in the GCP Console, and supply the credentials to log in.

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• Assign the finance team only the Billing Account User role on the billing account.
• Assign the engineering team only the Billing Account User role on the billing account.
• Assign the finance team the Billing Account User role on the billing account and the Project Billing Manager role on the organization.
• Assign the engineering team the Billing Account User role on the billing account and the Project Billing Manager role on the organization.

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• Deploy the monitoring pod in a StatefulSet object.
• Deploy the monitoring pod in a DaemonSet object.
• Reference the monitoring pod in a Deployment object.
• Reference the monitoring pod in a cluster initializer at the GKE cluster creation time.

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• Enable the Node Auto-Repair feature for your GKE cluster.
• Enable the Node Auto-Upgrades feature for your GKE cluster.
• Select the latest available cluster version for your GKE cluster.
• Select 'Container-Optimized OS (cos)' as a node image for your GKE cluster.

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• Multi-Regional Storage.
• Regional Storage.
• Nearline Storage.
• Coldline Storage.

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• Use Cloud Storage Object Lifecycle Management using Age conditions with SetStorageClass and Delete actions. Set the SetStorageClass action to 90 days and the Delete action to 275 days (365-90).
• Use Cloud Storage Object Lifecycle Management using Age conditions with SetStorageClass and Delete actions. Set the SetStorageClass action to 90 days and the Delete action to 365 days.
• Use gsutil rewrite and set the Delete action to 275 days (365-90).
• Use gsutil rewrite and set the Delete action to 365 days.

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• 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.

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• Create an instance template for the instances. Set the 'Automatic Restart' to on. Set the 'On-host maintenance' to Migrate VM instance. Add the instance template to an instance group.
• Create an instance template for the instances. Set 'Automatic Restart' to off. Set 'On-host maintenance' to Terminate VM instances. Add the instance template to an instance group.
• Create an instance group for the instances. Set the 'Autohealing' health check to healthy (HTTP).
• Create an instance group for the instance. Verify that the 'Advanced creation options' setting for 'do not retry machine creation' is set to off.

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• 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.

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• Use gcloud config configurations describe to review the output.
• Use gcloud config configurations activate and gcloud config list to review the output.
• Use kubectl config get-contexts to review the output.
• Use kubectl config use-context and kubectl config view to review the output.

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• 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.

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• 0.0.0.0/0.
• 10.0.0.0/8.
• 172.16.0.0/12.
• 192.168.0.0/16.

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• Run gcloud app restore.
• On the App Engine page of the GCP Console, select the application that needs to be reverted and click Revert.
• On the App Engine Versions page of the GCP Console, route 100% of the traffic to the previous version.
• Deploy the original version as a separate application. Then go to App Engine settings and split traffic between applications so that the original version serves 100% of the requests.

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• Create an HTTP load balancer with a backend configuration that references an existing instance group. Set the health check to healthy (HTTP).
• Create an HTTP load balancer with a backend configuration that references an existing instance group. Define a balancing mode and set the maximum RPS to 10.
• Create a Managed Instance Group. Set the Autohealing health check to healthy (HTTP).
• Create a Managed Instance Group. Verify that the autoscaling setting is on.

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• When creating the VM via the web console, specify the service account under the 'Identity and API Access' section.
• Download a JSON Private Key for the service account. On the Project Metadata, add that JSON as the value for the key compute-engine-service-account.
• Download a JSON Private Key for the service account. On the Custom Metadata of the VM, add that JSON as the value for the key compute-engine-service-account.
• Download a JSON Private Key for the service account. After creating the VM, ssh into the VM and save the JSON under ~/.gcloud/compute-engine-service-account.json.

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• Download the private key from the service account, and add it to each VMs custom metadata.
• Download the private key from the service account, and add the private key to each VM's SSH keys.
• Grant the service account the IAM role of Compute Storage Admin in the project called proj-vm.
• When creating the VMs, set the service account's API scope for Compute Engine to read/write.

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• 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.

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• Generate a new SSH key pair. Give the private key to each member of your team. Configure the public key in the metadata of each instance.
• Ask each member of the team to generate a new SSH key pair and to send you their public key. Use a configuration management tool to deploy those keys on each instance.
• Ask each member of the team to generate a new SSH key pair and to add the public key to their Google account. Grant the 'compute.osAdminLogin' role to the Google group corresponding to this team.
• Generate a new SSH key pair. Give the private key to each member of your team. Configure the public key as a project-wide public SSH key in your Cloud Platform project and allow project-wide public SSH keys on each instance.

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• Deploy the container on Cloud Run.
• Deploy the container on Cloud Run on GK.
• Deploy the container on App Engine Flexible.
• Deploy the container on Google Kubernetes Engine, with cluster autoscaling and horizontal pod autoscaling enabled.

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• 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.

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• Cloud SQL.
• Cloud Spanner.
• Cloud Firestore.
• Cloud Datastore.

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• Add the cluster's API as a new Type Provider in Deployment Manager, and use the new type to create the DaemonSet.
• Use the Deployment Manager Runtime Configurator to create a new Config resource that contains the DaemonSet definition.
• With Deployment Manager, create a Compute Engine instance with a startup script that uses kubectl to create the DaemonSet.
• In the cluster's definition in Deployment Manager, add a metadata that has kube-system as key and the DaemonSet manifest as value.

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• Rely on live migration to move the workload to a machine with more memory.
• Use gcloud to add metadata to the V. 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.

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• 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.

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• Contact [email protected] 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.

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• Use kubectl app deploy <dockerfilename>.
• Use gcloud app deploy <dockerfilename>.
• 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.

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• 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.

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• 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.

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• Enable Cloud CDN on the website frontend.
• Enable 'Share publicly' on the PDF file objects.
• Set Content-Type metadata to application/pdf on the PDF file objects.
• Add a label to the storage bucket with a key of Content-Type and value of application/pdf.

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• Reserve the IP 10.0.3.21 as a static internal IP address using gcloud and assign it to the licensing server.
• Reserve the IP 10.0.3.21 as a static public IP address using gcloud and assign it to the licensing server.
• Use the IP 10.0.3.21 as a custom ephemeral IP address and assign it to the licensing server.
• Start the licensing server with an automatic ephemeral IP address, and then promote it to a static internal IP address.

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• Manual Scaling with 3 instances.
• Basic Scaling with min_instances set to 3.
• Basic Scaling with max_instances set to 3.
• Automatic Scaling with min_idle_instances set to 3.

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• Project Editor.
• Storage Admin.
• Storage Object Admin.
• Storage Object Creator.

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• gcloud deployment-manager deployments create –config .
• gcloud deployment-manager deployments update –config .
• gcloud deployment-manager resources create –config .
• gcloud deployment-manager resources update –config .

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• 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.

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• 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.

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• 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.

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• Run gcloud iam roles list. Review the output section.
• Run gcloud iam service-accounts list. Review the output section.
• Navigate to the project and then to the IAM section in the GCP Console. Review the members and roles.
• Navigate to the project and then to the Roles section in the GCP Console. Review the roles and status.

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• Select Google Kubernetes Engine. Use a single-node cluster with a small instance type.
• Select Google Kubernetes Engine. Use a three-node cluster with micro instance types.
• Select Compute Engine. Use preemptible VM instances of the appropriate standard machine type.
• Select Compute Engine. Use VM instance types that support micro bursting.

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• Select Cloud SQL (MySQL). Verify that the enable binary logging option is selected.
• Select Cloud SQL (MySQL). Select the create failover replicas option.
• Select Cloud Spanner. Set up your instance with 2 nodes.
• Select Cloud Spanner. Set up your instance as multi-regional.

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• 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. 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 Google Cloud. 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.

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• 1. Create an IAM entry for each data scientist's user account. 2. Assign the BigQuery jobUser role to the group.
• 1. Create an IAM entry for each data scientist's user account. 2. Assign the BigQuery dataViewer user role to the group.
• 1. Create a dedicated Google group in Cloud Identity. 2. Add each data scientist's user account to the group. 3. Assign the BigQuery jobUser role to the group.
• 1. Create a dedicated Google group in Cloud Identity. 2. Add each data scientist's user account to the group. 3. Assign the BigQuery dataViewer user role to the group.

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• 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.

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• Cloud Pub/Sub, Cloud Dataflow, Cloud Datastore, BigQuery.
• Firebase Messages, Cloud Pub/Sub, Cloud Spanner, BigQuery.
• Cloud Pub/Sub, Cloud Storage, BigQuery, Cloud Bigtable.
• Cloud Pub/Sub, Cloud Dataflow, Cloud Bigtable, BigQuery.

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• 1. Give the BigQuery Data Editor role on the platform-logs dataset to the service accounts used by your instances. 2. Update your instances' metadata to add the following value: logs-destination: bq://platform-logs.
• 1. In Cloud Logging, create a logs export with a Cloud Pub/Sub topic called logs as a sink. 2. Create a Cloud Function that is triggered by messages in the logs topic. 3. Configure that Cloud Function to drop logs that are not from Compute Engine and to insert Compute Engine logs in the platform-logs dataset.
• 1. In Cloud Logging, create a filter to view only Compute Engine logs. 2. Click Create Export. 3. Choose BigQuery as Sink Service, and the platform-logs dataset as Sink Destination.
• 1. Create a Cloud Function that has the BigQuery User role on the platform-logs dataset. 2. Configure this Cloud Function to create a BigQuery Job that executes this query: INSERT INTO dataset.platform-logs (timestamp, log) SELECT timestamp, log FROM compute.logs WHERE timestamp > DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY) 3. Use Cloud Scheduler to trigger this Cloud Function once a day.

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• 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.

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• 1. Go to the Logs ingestion window in Stackdriver Logging, and disable the log source for the GKE container resource.
• 1. Go to the Logs ingestion window in Stackdriver Logging, and disable the log source for the GKE Cluster Operations resource.
• 1. Go to the GKE console, and delete existing clusters. 2. Recreate a new cluster. 3. Clear the option to enable legacy Stackdriver Logging.
• 1. Go to the GKE console, and delete existing clusters. 2. Recreate a new cluster. 3. Clear the option to enable legacy Stackdriver Monitoring.

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• 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.

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• 1. In GKE, create a Service of type LoadBalancer that uses the application's Pods as backend. 2. Set the service's externalTrafficPolicy to Cluster. 3. Configure the Compute Engine instance to use the address of the load balancer that has been created.
• 1. In GKE, create a Service of type NodePort that uses the application's Pods as backend. 2. Create a Compute Engine instance called proxy with 2 network interfaces, one in each VPC. 3. Use iptables on this instance to forward traffic from gce-network to the GKE nodes. 4. Configure the Compute Engine instance to use the address of proxy in gce-network as endpoint.
• 1. In GKE, create a Service of type LoadBalancer that uses the application's Pods as backend. 2. Add an annotation to this service: cloud.google.com/load-balancer-type: Internal 3. Peer the two VPCs together. 4. Configure the Compute Engine instance to use the address of the load balancer that has been created.
• 1. In GKE, create a Service of type LoadBalancer that uses the application's Pods as backend. 2. Add a Cloud Armor Security Policy to the load balancer that whitelists the internal IPs of the MIG's instances. 3. Configure the Compute Engine instance to use the address of the load balancer that has been created.

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• 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.

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• 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.

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• After the VM has been created, use your Google Account credentials to log in into the VM.
• After the VM has been created, use gcloud compute reset-windows-password to retrieve the login credentials for the VM.
• When creating the VM, add metadata to the instance using 'windows-password' as the key and a password as the value.
• After the VM has been created, download the JSON Private Key for the default Compute Engine service account. Use the credentials in the JSON file to log in to the VM.

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• 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.

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• Run gcloud projects list to get the project ID, and then run gcloud services list --project .
• Run gcloud init to set the current project to my-project, and then run gcloud services list --available.
• Run gcloud info to view the account value, and then run gcloud services list --account .
• Run gcloud projects describe to verify the project value, and then run gcloud services list --available.

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• 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.

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• 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.

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• 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.

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• Verify that both projects are in a GCP Organization. Create a new VPC and add all instances.
• Verify that both projects are in a GCP Organization. Share the VPC from one project and request that the Compute Engine instances in the other project use this shared VPC.
• Verify that you are the Project Administrator of both projects. Create two new VPCs and add all instances.
• Verify that you are the Project Administrator of both projects. Create a new VPC and add all instances.

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• Create a custom role with view-only project permissions. Add the user's account to the custom role.
• Create a custom role with view-only service permissions. Add the user's account to the custom role.
• Select the built-in IAM project Viewer role. Add the user's account to this role.
• Select the built-in IAM service Viewer role. Add the user's account to this role.

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• Ask your ML team to add the accelerator: gpu annotation to their pod specification.
• Recreate all the nodes of the GKE cluster to enable GPUs on all of them.
• Create your own Kubernetes cluster on top of Compute Engine with nodes that have GPUs. Dedicate this cluster to your ML team.
• Add a new, GPU-enabled, node pool to the GKE cluster. Ask your ML team to add the cloud.google.com/gke -accelerator: nvidia-tesla-p100 nodeSelector to their pod specification.

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• 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.

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• Enable Cloud Identity in the GCP Console for your domain.
• Grant them the required IAM roles using their G Suite email address.
• Create a CSV sheet with all users' email addresses. Use the gcloud command line tool to convert them into Google Cloud Platform accounts.
• In the G Suite console, add the users to a special group called cloud-console-users@yourdomain.com. Rely on the default behavior of the Cloud Platform to grant users access if they are members of this group.

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• 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.

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• Load data in Cloud Datastore and run a SQL query against it.
• Create a BigQuery table and load data in BigQuery. Run a SQL query on this table and drop this table after you complete your request.
• Create external tables in BigQuery that point to Cloud Storage buckets and run a SQL query on these external tables to complete your request.
• Create a Hadoop cluster and copy the AVRO file to NDFS by compressing it. Load the file in a hive table and provide access to your analysts so that they can run SQL queries.

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• 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.

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• Add the network tag allow-udp-636 to the VM instance running the LDAP server.
• Create a route called allow-udp-636 and set the next hop to be the VM instance running the LDAP server.
• Add a network tag of your choice to the instance. Create a firewall rule to allow ingress on UDP port 636 for that network tag.
• Add a network tag of your choice to the instance running the LDAP server. Create a firewall rule to allow egress on UDP port 636 for that network tag.

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• 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.

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• 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.

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• Add a bucket lifecycle rule that archives data with newer versions after 30 days to Coldline Storage.
• Add a bucket lifecycle rule that archives data with newer versions after 30 days to Nearline Storage.
• Add a bucket lifecycle rule that archives data from regional storage after 30 days to Coldline Storage.
• Add a bucket lifecycle rule that archives data from regional storage after 30 days to Nearline Storage.

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

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• Select Multi-Regional Storage. Add a bucket lifecycle rule that archives data after 30 days to Coldline Storage.
• Select Multi-Regional Storage. Add a bucket lifecycle rule that archives data after 30 days to Nearline Storage.
• Select Regional Storage. Add a bucket lifecycle rule that archives data after 30 days to Nearline Storage.
• Select Regional Storage. Add a bucket lifecycle rule that archives data after 30 days to Coldline Storage.

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• Go to Data Catalog and search for employee_ssn in the search box.
• Write a shell script that uses the bq command line tool to loop through all the projects in your organization.
• Write a script that loops through all the projects in your organization and runs a query on INFORMATION_SCHEMA.COLUMNS view to find the employee_ssn column.
• Write a Cloud Dataflow job that loops through all the projects in your organization and runs a query on INFORMATION_SCHEMA.COLUMNS view to find employee_ssn column.

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• 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.

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• Open the Cloud Spanner console to review configurations.
• Open the IAM & admin console to review IAM policies for Cloud Spanner roles.
• Go to the Stackdriver Monitoring console and review information for Cloud Spanner.
• Go to the Stackdriver Logging console, review admin activity logs, and filter them for Cloud Spanner IAM roles.

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• 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.

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• Use Binary Authorization and whitelist only the container images used by your customers' Pods.
• Use the Container Analysis API to detect vulnerabilities in the containers used by your customers' Pods.
• Create a GKE node pool with a sandbox type configured to gvisor. Add the parameter runtimeClassName: gvisor to the specification of your customers' Pods.
• Use the cos_containerd image for your GKE nodes. Add a nodeSelector with the value cloud.google.com/gke-os-distribution: cos_containerd to the specification of your customers' Pods.

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• Remove the profile_picture field from the table.
• Add a secondary index on the person_id column.
• Change the primary key to not have monotonically increasing values.
• Create a secondary index using the following Data Definition Language (DDL):

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• 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.

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• 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.

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• Use a Shielded VM.
• Use a Preemptible VM.
• Use a sole-tenant node.
• Enable deletion protection on the instance.

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• 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.

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• Export Cloud Datastore data using gcloud datastore export.
• Create a Cloud Datastore index using gcloud datastore indexes create.
• Install the google-cloud-sdk-datastore-emulator component using the apt get install command.
• Install the cloud-datastore-emulator component using the gcloud components install command.

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

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• 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.

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• Add the user to roles/iam.roleAdmin role.
• Add the user to roles/iam.securityAdmin role.
• Add the user to roles/iam.serviceAccountUser role.
• Add the user to roles/iam.serviceAccountAdmin role.

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• Cold Storage.
• Nearline Storage.
• Regional Storage.
• Multi-Regional Storage.

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• Enable node auto-provisioning on the GKE cluster.
• Create a VerticalPodAutscaler for those workloads.
• Create a node pool with preemptible VMs and GPUs attached to those VMs.
• Create a node pool of instances with GPUs, and enable autoscaling on this node pool with a minimum size of 1.

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• 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.

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• Create a new project, enable the Compute Engine and Cloud SQL APIs in that project, and replicate the setup you have created in the development environment.
• Create a new production subnet in the existing VPC and a new production Cloud SQL instance in your existing project, and deploy your application using those resources.
• Create a new project, modify your existing VPC to be a Shared VPC, share that VPC with your new project, and replicate the setup you have in the development environment in that new project in the Shared VP.
• Ask the security team to grant you the Project Editor role in an existing production project used by another division of your company. Once they grant you that role, replicate the setup you have in the development environment in that project.

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• 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.

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• 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.

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• 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.list permission. Perform the export of logs to Cloud Storage.
• Assign the auditor's IAM user to a custom role that has logging.privateLogEntries.list permission. Direct the auditor to also review the logs for changes to Cloud IAM policy.

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• Navigate to Stackdriver Logging and select resource.labels.project_id="*".
• Create a Stackdriver Logging Export with a Sink destination to a BigQuery dataset. Configure the table expiration to 60 days.
• Create a Stackdriver Logging Export with a Sink destination to Cloud Storage. Create a lifecycle rule to delete objects after 60 days.
• Configure a Cloud Scheduler job to read from Stackdriver and store the logs in BigQuery. Configure the table expiration to 60 days.

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• 1. Verify that you are assigned the Project Owners IAM role for this project. 2. Locate the project in the GCP console, click Shut down and then enter the project I.
• 1. Verify that you are assigned the Project Owners IAM role for this project. 2. Switch to the project in the GCP console, locate the resources and delete them.
• 1. Verify that you are assigned the Organizational Administrator IAM role for this project. 2. Locate the project in the GCP console, enter the project ID and then click Shut down.
• 1. Verify that you are assigned the Organizational Administrators IAM role for this project. 2. Switch to the project in the GCP console, locate the resources and delete them.

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• Give project owner for web-applications appropriate roles to crm-databases-proj.
• Give project owner role to crm-databases-proj and the web-applications project.
• Give project owner role to crm-databases-proj and bigquery.dataViewer role to web-applications.
• Give bigquery.dataViewer role to crm-databases-proj and appropriate roles to web-applications.

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• View System Event Logs in Stackdriver. Search for the user's email as the principal.
• View System Event Logs in Stackdriver. Search for the service account associated with the user.
• View Data Access audit logs in Stackdriver. Search for the user's email as the principal.
• View the Admin Activity log in Stackdriver. Search for the service account associated with the user.

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• 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.

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• 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.

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

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• Create an instance template that contains valid syntax which will be used by the instance group. Delete any persistent disks with the same name as instance names.
• Create an instance template that contains valid syntax that will be used by the instance group. Verify that the instance name and persistent disk name values are not the same in the template.
• Verify that the instance template being used by the instance group contains valid syntax. Delete any persistent disks with the same name as instance names. Set the disks.autoDelete property to true in the instance template.
• Delete the current instance template and replace it with a new instance template. Verify that the instance name and persistent disk name values are not the same in the template. Set the disks.autoDelete property to true in the instance template.

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• 1. Build an instruction guide to install Cassandra on Google Cloud. 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.

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• 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.

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• Create a cron job that runs on a scheduled basis to review Cloud Monitoring metrics, and then resize the Spanner instance accordingly.
• Create a Cloud Monitoring alerting policy to send an alert to oncall SRE emails when Cloud Spanner CPU exceeds the threshold. SREs would scale resources up or down accordingly.
• Create a Cloud Monitoring alerting policy to send an alert to Google Cloud Support email when Cloud Spanner CPU exceeds your threshold. Google support would scale resources up or down accordingly.
• Create a Cloud Monitoring alerting policy to send an alert to webhook when Cloud Spanner CPU is over or under your threshold. Create a Cloud Function that listens to HTTP and resizes Spanner resources accordingly.

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

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• For each Google Cloud product in the solution, review the pricing details on the products pricing page. Use the pricing calculator to total the monthly costs for each Google Cloud product.
• For each Google Cloud product in the solution, review the pricing details on the products pricing page. Create a Google Sheet that summarizes the expected monthly costs for each product.
• Provision the solution on Google Cloud. Leave the solution provisioned for 1 week. Navigate to the Billing Report page in the Cloud Console. Multiply the 1 week cost to determine the monthly costs.
• Provision the solution on Google Cloud. Leave the solution provisioned for 1 week. Use Cloud Monitoring to determine the provisioned and used resource amounts. Multiply the 1 week cost to determine the monthly costs.

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• 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.

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• 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.

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• 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.

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• Ask the other team to grant your default App Engine Service account the role of BigQuery Job User.
• Ask the other team to grant your default App Engine Service account the role of BigQuery Data Viewer.
• In Cloud IAM of your project, ensure that the default App Engine service account has the role of BigQuery Data Viewer.
• In Cloud IAM of your project, grant a newly created service account from the other team the role of BigQuery Job User in your project.

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• Create a Compute Engine snapshot of your base VM. Create your images from that snapshot.
• Create a Compute Engine snapshot of your base VM. Create your instances from that snapshot.
• Create a custom Compute Engine image from a snapshot. Create your images from that image.
• Create a custom Compute Engine image from a snapshot. Create your instances from that image.

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• 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.

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• 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.

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• Assign appropriate access for Google services to the service account used by the Compute Engine VM.
• Create a service account with appropriate access for Google services, and configure the application to use this account.
• Store credentials for service accounts with appropriate access for Google services in a config file, and deploy this config file with your application.
• Store credentials for your user account with appropriate access for Google services in a config file, and deploy this config file with your application.

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• Using the Cloud SDK, create a new project, enable the Compute Engine API in that project, and then create the instance specifying your new project.
• Enable the Compute Engine API in the Cloud Console, use the Cloud SDK to create the instance, and then use the --project flag to specify a new project.
• Using the Cloud SDK, create the new instance, and use the --project flag to specify the new project. Answer yes when prompted by Cloud SDK to enable the Compute Engine API.
• Enable the Compute Engine API in the Cloud Console. Go to the Compute Engine section of the Console to create a new instance, and look for the Create In A New Project option in the creation form.

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• Migrate the workload to a Compute Engine Preemptible VM.
• Migrate the workload to a Google Kubernetes Engine cluster with Preemptible nodes.
• Migrate the workload to a Compute Engine VM. Start and stop the instance as needed.
• Create an Instance Template with Preemptible VMs On. Create a Managed Instance Group from the template and adjust Target CPU Utilization. Migrate the workload.

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• 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.

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• Set the europe-west1-d zone as the default zone using the gcloud config subcommand.
• In the Settings page for Compute Engine under Default location, set the zone to europe-west1-d.
• In the CLI installation directory, create a file called default.conf containing zone=europe-west1-d.
• Create a Metadata entry on the Compute Engine page with key compute/zone and value europe-west1-d.

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• 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.

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• Enable API and then share charts from project A, B, and C.
• Enable API and then give the metrics.reader role to projects A, B, and C.
• Enable API and then use default dashboards to view all projects in sequence.
• Enable API, create a workspace under project A, and then add projects B and C.

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• Configure Billing Data Export to BigQuery and visualize the data in Data Studio.
• Visit the Cost Table page to get a CSV export and visualize it using Data Studio.
• Fill all resources in the Pricing Calculator to get an estimate of the monthly cost.
• Use the Reports view in the Cloud Billing Console to view the desired cost information.

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• Create the instance without a public IP address.
• Create the instance with Private Google Access enabled.
• Create a deny-all egress firewall rule on the VPC network.
• Create a route on the VPC to route all traffic to the instance over the VPN tunnel.

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• 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 changes in a development environment, run gcloud compute instances list, and then save the output in a shared Storage bucket.
• Apply the changes in a development environment, run gcloud compute instances list, and then save the output in Cloud Source Repositories.

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• 1. Update your instances' metadata to add the following value: snapshot-schedule: 0 1 * * * 2. Update your instances' metadata to add the following value: snapshot-retention: 30.
• 1. In the Cloud Console, go to the Compute Engine Disks page and select your instance's disk. 2. In the Snapshot Schedule section, select Create Schedule and configure the following parameters: Schedule frequency: Daily. Start time: 1:00 AM - 2:00 AM. Autodelete snapshots after: 30 days.
• 1. Create a Cloud Function that creates a snapshot of your instance's disk. 2. Create a Cloud Function that deletes snapshots that are older than 30 days. 3. Use Cloud Scheduler to trigger both Cloud Functions daily at 1:00 AM.
• 1. Create a bash script in the instance that copies the content of the disk to Cloud Storage. 2. Create a bash script in the instance that deletes data older than 30 days in the backup Cloud Storage bucket. 3. Configure the instance's crontab to execute these scripts daily at 1:00 AM.

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• 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.

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• Create a dataflow job that copies data from Cloud Bigtable and Cloud Storage for specific users.
• Create a dataflow job that copies data from Cloud Bigtable and Cloud Spanner for specific users.
• Create a Cloud Dataproc cluster that runs a Spark job to extract data from Cloud Bigtable and Cloud Storage for specific users.
• Create two separate BigQuery external tables on Cloud Storage and Cloud Bigtable. Use the BigQuery console to join these tables through user fields, and apply appropriate filters.

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• Create Compute Engine resources in us-central1-b. Balance the load across both us-central1-a and us-central1-b.
• Create a Managed Instance Group and specify us-central1-a as the zone. Configure the Health Check with a short Health Interval.
• Create an HTTP(S) Load Balancer. Create one or more global forwarding rules to direct traffic to your VMs.
• Perform regular backups of your application. Create a Cloud Monitoring Alert and be notified if your application becomes unavailable. Restore from backups when notified.

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• 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.

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• Create a new subnet in the same region as the subnet being used.
• Add an alias IP range to the subnet used by the GKE clusters.
• Create a new VPC, and set up VPC peering with the existing VP.
• Expand the CIDR range of the relevant subnet for the cluster.

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• 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.

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• 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.

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• 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.

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• 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.

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• Set up a policy that uses Nearline storage for 30 days and then moves to Archive storage for three years.
• Set up a policy that uses Standard storage for 30 days and then moves to Archive storage for three years.
• Set up a policy that uses Nearline storage for 30 days, then moves the Coldline for one year, and then moves to Archive storage for two years.
• Set up a policy that uses Standard storage for 30 days, then moves to Coldline for one year, and then moves to Archive storage for two years.

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• Enable the Identity Aware Proxy API on the project.
• Scan the bucket 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.

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• 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.

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• 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.

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• Grant all members of the DevOps team the role of Project Editor on the organization level.
• Grant all members of the DevOps team the role of Project Editor on the production project.
• Create a custom role that combines the required permissions. Grant the DevOps team the custom role on the production project.
• Create a custom role that combines the required permissions. Grant the DevOps team the custom role on the organization level.

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• Build a lifecycle policy to delete Cloud Storage objects after 45 days.
• Use signed URLs to allow suppliers limited time access to store their objects.
• Set up an SFTP server for your application, and create a separate user for each supplier.
• Build a Cloud function that triggers a timer of 45 days to delete objects that have expired.
• Develop a script that loops through all Cloud Storage buckets and deletes any buckets that are older than 45 days.

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

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• Enable Audit Logs for all APIs that are related to data storage.
• Review the IAM permissions for any role that allows for data access.
• Review the Identity-Aware Proxy settings for each resource.
• Create a Data Loss Prevention job.

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• 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.

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• 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.

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• Grant the financial team the IAM role of Billing Account User 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.

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• Create a Service Account in your own project, and grant this Service Account access to BigQuery 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.

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

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• 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.

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• Create a Pub/Sub topic, and enable a Cloud Storage trigger for the Pub/Sub topic. Create an application that sends all medical images to the Pub/Sub topic.
• Deploy a Dataflow job from the batch template, Datastore to Cloud Storage. Schedule the batch job on the desired interval.
• Create a script that uses the gsutil command line interface to synchronize the on-premises storage with Cloud Storage. Schedule the script as a cron job.
• In the Cloud Console, go to Cloud Storage. Upload the relevant images to the appropriate bucket.

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• 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.
• Assign the appropriate permissions, and the use Cloud Monitoring to review metrics.
• Use the export logs API to provide the Admin Activity Audit Logs in the format they want.

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• 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.json.
• Place the private key file in your home directory and rename it to GOOGLE_APPLICATION_CREDENTIALS.

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• Set up an export job for the first of the month. Write the export file to an Archive class Cloud Storage bucket.
• Save the automatic first-of-the-month backup for three years. Store the backup file in an Archive class Cloud Storage bucket.
• Set up an on-demand backup for the first of the month. Write the backup to an Archive class Cloud Storage bucket.
• Convert the automatic first-of-the-month backup to an export file. Write the export file to a Coldline class Cloud Storage bucket.

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• 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.

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• Use Cloud Bigtable for data storage.
• Use Cloud SQL for data storage.
• Use Cloud Spanner for data storage.
• Use Firestore for data storage.

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• 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.

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• Create and deploy a Custom Resource Definition per microservice.
• Create and deploy a Docker Compose File.
• Create and deploy a Job per microservice.
• Create and deploy a Deployment per microservice.

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• When creating the instances, specify a Service Account for each instance.
• When creating the instances, assign the name of each Service Account as instance metadata.
• After starting the instances, use gcloud compute instances update to specify a Service Account for each instance.
• After starting the instances, use gcloud compute instances update to assign the name of the relevant Service Account as instance metadata.

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• Create a Cloud Bigtable cluster, and use the HBase API.
• Deploy MongoDB Atlas from the Google Cloud Marketplace.
• Download a MongoDB installation package, and run it on Compute Engine instances.
• Download a MongoDB installation package, and run it on a Managed Instance Group.

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• Create a Data Studio dashboard that uses the related BigQuery tables as a source and give the BI team view access to the Data Studio dashboard.
• Create a Service Account for the BI team and distribute a new private key to each member of the BI team.
• Use Cloud Scheduler to schedule a batch Dataflow job to copy the data from BigQuery to the BI team's internal data warehouse.
• Assign the IAM role of BigQuery User to a Google Group that contains the members of the BI team.

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• 1. Create a single VPC with a subnet for the DMZ and a subnet for the LAN. 2. Set up firewall rules to open up relevant traffic between the DMZ and the LAN subnets, and another firewall rule to allow public ingress traffic for the DMZ.
• 1. Create a single VPC with a subnet for the DMZ and a subnet for the LAN. 2. Set up firewall rules to open up relevant traffic between the DMZ and the LAN subnets, and another firewall rule to allow public egress traffic for the DMZ.
• 1. Create a VPC with a subnet for the DMZ and another VPC with a subnet for the LAN. 2. Set up firewall rules to open up relevant traffic between the DMZ and the LAN subnets, and another firewall rule to allow public ingress traffic for the DMZ.
• 1. Create a VPC with a subnet for the DMZ and another VPC with a subnet for the LAN. 2. Set up firewall rules to open up relevant traffic between the DMZ and the LAN subnets, and another firewall rule to allow public egress traffic for the DMZ.

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