Free Practice Questions for the Google Cloud Certified Professional-Cloud-Architect Exam (2026 Updated)

Create a dump of the on-premises MySQL master server, and then shut it down, upload it to the cloud environment, and load into a new MySQL cluster.

Setup a MySQL replica server/slave in the cloud environment, and configure it for asynchronous replication from the MySQL master server on-premises until cutover.

Create a new MySQL cluster in the cloud, configure applications to begin writing to both on-premises and cloud MySQL masters, and destroy the original cluster at cutover.

Create a dump of the MySQL replica server into the cloud environment, load it into: Google Cloud Datastore, and configure applications to read/write to Cloud Datastore at cutover.

Create a token and pass it in as an environment variable to func_display. When invoking func_query, include the token in the request Pass the same token to func _query and reject the invocation if the tokens are different.

Make func_query ' Require authentication. ' Create a unique service account and associate it to func_display. Grant the service account invoker role for func_query. Create an id token in func_display and include the token to the request when invoking func_query.

Make func _query ' Require authentication ' and only accept internal traffic. Create those two functions in the same VPC. Create an ingress firewall rule for func_query to only allow traffic from func_display.

Create those two functions in the same project and VPC. Make func_query only accept internal traffic. Create an ingress firewall for func_query to only allow traffic from func_display. Also, make sure both functions use the same service account.

Use BigQuery as the data warehouse. Connect all vehicles to the network and stream data into BigQuery using Cloud Pub/Sub and Cloud Dataflow. Use Google Data Studio for analysis and reporting.

Use BigQuery as the data warehouse. Connect all vehicles to the network and upload gzip files to a Multi-Regional Cloud Storage bucket using gcloud. Use Google Data Studio for analysis and reporting.

Use Cloud Dataproc Hive as the data warehouse. Upload gzip files to a MultiRegional Cloud Storage

bucket. Upload this data into BigQuery using gcloud. Use Google data Studio for analysis and reporting.

Use Cloud Dataproc Hive as the data warehouse. Directly stream data into prtitioned Hive tables. Use Pig scripts to analyze data.

Request Transfer Appliances from Google Cloud, export the data to appliances, and return the appliances to Google Cloud.

Configure the Storage Transfer service from Google Cloud to send the data from your data center to Cloud Storage

Make sure there are no other users consuming the 1 Gbps link, and use multi-thread transfer to upload the data to Cloud Storage.

Export files to an encrypted USB device, send the device to Google Cloud, and request an import of the data to Cloud Storage

Evaluate the impact of migrating their current batch ETL code to Cloud Dataflow.

Write a schema migration plan to denormalize data for better performance in BigQuery.

Draw an architecture diagram that shows how to move from a single MySQL database to a MySQL cluster.

Load 10 TB of analytics data from a previous game into a Cloud SQL instance, and run test queries against the full dataset to confirm that they complete successfully.

Integrate Cloud Armor to defend against possible SQL injection attacks in analytics files uploaded to Cloud Storage.

Open a support case regarding the CVE and chat with the support engineer.

Read the CVEs from the Google Cloud Status Dashboard to understand the impact.

Read the CVEs from the Google Cloud Platform Security Bulletins to understand the impact

Post a question regarding the CVE in Stack Overflow to get an explanation

Post a question regarding the CVE in a Google Cloud discussion group to get an explanation

Set up a streaming Cloud Dataflow job, receiving data by the ingestion process. Clean the data in a Cloud Dataflow pipeline.

Create a Cloud Function that reads data from BigQuery and cleans it. Trigger it. Trigger the Cloud Function from a Compute Engine instance.

Create a SQL statement on the data in BigQuery, and save it as a view. Run the view daily, and save the result to a new table.

Use Cloud Dataprep and configure the BigQuery tables as the source. Schedule a daily job to clean the data.

Create a Cloud Storage lifecycle rule with Age: “30”, Storage Class: “Standard”, and Action: “Set to Coldline”, and create a second GCS life-cycle rule with Age: “365”, Storage Class: “Coldline”, and Action: “Delete”.

Create a Cloud Storage lifecycle rule with Age: “30”, Storage Class: “Coldline”, and Action: “Set to Nearline”, and create a second GCS life-cycle rule with Age: “91”, Storage Class: “Coldline”, and Action: “Set to Nearline”.

Create a Cloud Storage lifecycle rule with Age: “90”, Storage Class: “Standard”, and Action: “Set to Nearline”, and create a second GCS life-cycle rule with Age: “91”, Storage Class: “Nearline”, and Action: “Set to Coldline”.

Create a Cloud Storage lifecycle rule with Age: “30”, Storage Class: “Standard”, and Action: “Set to Coldline”, and create a second GCS life-cycle rule with Age: “365”, Storage Class: “Nearline”, and Action: “Delete”.

Configure Workload Identity and service accounts to be used by the application platform.

Use Kubernetes Secrets, which are obfuscated by default. Configure these Secrets to be used by the

application platform.

Configure Kubernetes Secrets to store the secret, enable Application-Layer Secrets Encryption, and use

Cloud Key Management Service (Cloud KMS) to manage the encryption keys. Configure these Secrets to

be used by the application platform.

Configure HashiCorp Vault on Compute Engine, and use customer managed encryption keys and Cloud

Key Management Service (Cloud KMS) to manage the encryption keys. Configure these Secrets to be used

by the application platform.

Grant the operations engineers access to use Google Cloud Shell.

Configure a VPN connection to GCP to allow SSH access to the cloud VMs.

Develop a new access request process that grants temporary SSH access to cloud VMs when an operations engineer needs to perform a task.

Have the development team build an API service that allows the operations team to execute specific remote procedure calls to accomplish their tasks.

Identify self-contained applications with external dependencies as a first move to the cloud.

Identify enterprise applications with internal dependencies and recommend these as a first move to the cloud.

Suggest moving their in-house databases to the cloud and continue serving requests to on-premise applications.

Recommend moving their message queuing servers to the cloud and continue handling requests to on-premise applications.

Create a service account (SA) in the legacy game ' s Google Cloud project, add this SA in the new game ' s IAM page, and then give it the Firebase Admin role in both projects

Create a service account (SA) in the legacy game ' s Google Cloud project, add a second SA in the new game ' s IAM page, and then give the Organization Admin role to both SAs

Create a service account (SA) in the legacy game ' s Google Cloud project, give it the Firebase Admin role, and then migrate the new game to the legacy game ' s project.

Create a service account (SA) in the lgacy game ' s Google Cloud project, give the SA the Organization Admin rule and then give it the Firebase Admin role in both projects

Use gsutil to batch move files in sequence.

Use gsutil to batch copy the files in parallel.

Use gsutil to extract the files as the first part of ETL.

Use gsutil to load the files as the last part of ETL.

Add a new Dedicated Interconnect connection.

Upgrade the bandwidth on the Dedicated Interconnect connection to 100 G.

Add three new Cloud VPN connections.

Add a new Carrier Peering connection.

Use regional managed instance groups and a global load balancer to increase performance because the

regional managed instance group can grow instances in each region separately based on traffic.

Use a global load balancer with a set of virtual machines that forward the requests to a closer group of

virtual machines managed by your operations team.

Use regional managed instance groups and a global load balancer to increase reliability by providing

automatic failover between zones in different regions.

Use a global load balancer with a set of virtual machines that forward the requests to a closer group of

virtual machines as part of a separate managed instance groups.

Create CPU Utilization and Request Latency as service level indicators.

Create GKE CPU Utilization and Memory Utilization as service level indicators.

Create Request Latency and Error Rate as service level indicators.

Create Server Uptime and Error Rate as service level indicators.

Create a cron script using gsutil to copy the files to a Coldline Storage bucket.

Create a cron script using gsutil to copy the files to a Regional Storage bucket.

Create a Cloud Storage Transfer Service Job to copy the files to a Coldline Storage bucket.

Create a Cloud Storage Transfer Service job to copy the files to a Regional Storage bucket.

Configure two Partner Interconnect connections in one metro (City), and make sure the Interconnect connections are placed in different metro zones.

Configure two VPN connections from on-premises to Google Cloud, and make sure the VPN devices on-premises are in separate racks.

Configure Direct Peering between EHR Healthcare and Google Cloud, and make sure you are peering at least two Google locations.

Configure two Dedicated Interconnect connections in one metro (City) and two connections in another metro, and make sure the Interconnect connections are placed in different metro zones.

Perform a mapping of the on-premises physical hardware cores and RAM to the nearest machine types in the cloud.

Recommend that Dress4Win deploy application servers to machine types that offer the highest RAM to CPU ratio available.

Recommend that Dress4Win deploy into production with the smallest instances available, monitor them over time, and scale the machine type up until the desired performance is reached.

Identify the number of virtual cores and RAM associated with the application server virtual machines align them to a custom machine type in the cloud, monitor performance, and scale the machine types up until the desired performance is reached.

Google Cloud Storage Coldline to store the data, and gsutil to access the data.

Google Cloud Storage Nearline to store the data, and gsutil to access the data.

Google Bigtabte with US or EU as location to store the data, and gcloud to access the data.

BigQuery to store the data, and a web server cluster in a managed instance group to access the data. Google Cloud SQL mirrored across two distinct regions to store the data, and a Redis cluster in a managed instance group to access the data.

Use Firestore for its scalable and flexible document-based database. Use collections to aggregate race data

by season and event.

Use Cloud Spanner for its scalability and ability to version schemas with zero downtime. Split race data

using season as a primary key.

Use BigQuery for its scalability and ability to add columns to a schema. Partition race data based on

season.

Use Cloud SQL for its ability to automatically manage storage increases and compatibility with MySQL. Use

separate database instances for each season.

Tests should scale well beyond the prior approaches.

Unit tests are no longer required, only end-to-end tests.

Tests should be applied after the release is in the production environment.

Tests should include directly testing the Google Cloud Platform (GCP) infrastructure.

Web applications deployed using App Engine standard environment

RabbitMQ deployed using an unmanaged instance group

Hadoop/Spark deployed using Cloud Dataproc Regional in High Availability mode

Jenkins, monitoring, bastion hosts, security scanners services deployed on custom machine types

Create a project for development and test and another for staging and production.

Create a network for development and test and another for staging and production.

Create one subnetwork for development and another for staging and production.

Create one project for development, a second for staging and a third for production.

Assign custom IAM roles to the Google Groups you created in order to enforce security requirements.

Encrypt data with a customer-supplied encryption key when storing files in Cloud Storage.

Assign custom IAM roles to the Google Groups you created in order to enforce security requirements.

Enable default storage encryption before storing files in Cloud Storage.

Assign predefined IAM roles to the Google Groups you created in order to enforce security requirements.

Utilize Google’s default encryption at rest when storing files in Cloud Storage.

Assign predefined IAM roles to the Google Groups you created in order to enforce security requirements. Ensure that the default Cloud KMS key is set before storing files in Cloud Storage.

Add a new Dedicated Interconnect connection

Upgrade the bandwidth on the Dedicated Interconnect connection to 100 G

Add three new Cloud VPN connections

Add a new Carrier Peering connection

Verify EHR ' s product usage against the list of compliant products on the Google Cloud compliance page.

Advise EHR to execute a Business Associate Agreement (BAA) with Google Cloud.

Use Firebase Authentication for EHR ' s user facing applications.

Implement Prometheus to detect and prevent security breaches on EHR ' s web-based applications.

Use GKE private clusters for all Kubernetes workloads.

Use a private cluster with a private endpoint with master authorized networks configured.

Use a public cluster with firewall rules and Virtual Private Cloud (VPC) routes.

Use a private cluster with a public endpoint with master authorized networks configured.

Use a public cluster with master authorized networks enabled and firewall rules.

Deploy Nginx and Tomcat using Cloud Deployment Manager to Compute Engine. Deploy a Cloud SQL server to replace MySQL. Deploy Jenkins using Cloud Deployment Manager.

Deploy Nginx and Tomcat using Cloud Launcher. Deploy a MySQL server using Cloud Launcher. Deploy Jenkins to Compute Engine using Cloud Deployment Manager scripts.

Migrate Nginx and Tomcat to App Engine. Deploy a Cloud Datastore server to replace the MySQL server in a high-availability configuration. Deploy Jenkins to Compute Engine using Cloud Launcher.

Migrate Nginx and Tomcat to App Engine. Deploy a MySQL server using Cloud Launcher. Deploy Jenkins to Compute Engine using Cloud Launcher.

Migrate the web application layer to App Engine, and MySQL to Cloud Datastore, and NAS to Cloud Storage. Deploy RabbitMQ, and deploy Hadoop servers using Deployment Manager.

Migrate RabbitMQ to Cloud Pub/Sub, Hadoop to BigQuery, and NAS to Compute Engine with Persistent Disk storage. Deploy Tomcat, and deploy Nginx using Deployment Manager.

Implement managed instance groups for Tomcat and Nginx. Migrate MySQL to Cloud SQL, RabbitMQ to Cloud Pub/Sub, Hadoop to Cloud Dataproc, and NAS to Compute Engine with Persistent Disk storage.

Implement managed instance groups for the Tomcat and Nginx. Migrate MySQL to Cloud SQL, RabbitMQ to Cloud Pub/Sub, Hadoop to Cloud Dataproc, and NAS to Cloud Storage.

They should run the end to end tests in the cloud staging environment to determine if the code is working as

intended.

They should enable google stack driver debugger on the application code to show errors in the code

They should add additional unit tests and production scale load tests on their cloud staging environment.

They should add canary tests so developers can measure how much of an impact the new release causes to latency

Replace RabbitMQ with Google Pub/Sub.

Downgrade MySQL to v5.7, which is supported by Cloud SQL for MySQL.

Resize compute resources to match predefined Compute Engine machine types.

Containerize the micro services and host them in Google Kubernetes Engine.

Use Stackdriver Trace to create a trace list analysis.

Use Stackdriver Monitoring to create a dashboard on the project’s activity.

Enable Cloud Identity-Aware Proxy in all projects, and add the group of Administrators as a member.

Use the Activity page in the GCP Console and Stackdriver Logging to provide the required insight.

Create an Organizational Policy with a constraint to allow external IP addresses only on the frontend Compute Engine instances.

Revoke the compute.networkAdmin role from all users in the project with front end instances.

Create an Identity and Access Management (IAM) policy that maps the IT staff to the compute.networkAdmin role for the organization.

Create a custom Identity and Access Management (IAM) role named GCE_FRONTEND with the compute.addresses.create permission.

Query logs in Cloud Logging.

Analyze the data via Cloud Profiler.

Instrument the code to use Cloud Trace.

Inspect data from Snapshot Debugger.

Cloud Bigtable

Cloud Spanner

BigQuery

Cloud Datastore

Increase the Pub/Sub Total Timeout retry value.

Move from a Pub/Sub subscriber pull model to a push model.

Turn off Pub/Sub message batching.

Create a backup Pub/Sub message queue.

Enable Binary Authorization on GKE, and sign containers as part of a CI/CD pipeline.

Configure Jenkins to utilize Kritis to cryptographically sign a container as part of a CI/CD pipeline.

Configure Container Registry to only allow trusted service accounts to create and deploy containers from the registry.

Configure Container Registry to use vulnerability scanning to confirm that there are no vulnerabilities before deploying the workload.

Migrate from CSV to binary format, migrate from FTP to SFTP transport, and develop machine learning analysis of metrics.

Migrate from FTP to streaming transport, migrate from CSV to binary format, and develop machine learning analysis of metrics.

Increase fleet cellular connectivity to 80%, migrate from FTP to streaming transport, and develop machine learning analysis of metrics.

Migrate from FTP to SFTP transport, develop machine learning analysis of metrics, and increase dealer local inventory by a fixed factor.

Move all the data into 1 zone, then launch a Cloud Dataproc cluster to run the job.

Move all the data into 1 region, then launch a Google Cloud Dataproc cluster to run the job.

Launch a cluster in each region to preprocess and compress the raw data, then move the data into a multi region bucket and use a Dataproc cluster to finish the job.

Launch a cluster in each region to preprocess and compress the raw data, then move the data into a region bucket and use a Cloud Dataproc cluster to finish the jo

Use Google App Engine with Google Cloud Endpoints. Focus on an API for dealers and partners.

Use Google App Engine with a JAX-RS Jersey Java-based framework. Focus on an API for the public.

Use Google App Engine with the Swagger (open API Specification) framework. Focus on an API for the public.

Use Google Container Engine with a Django Python container. Focus on an API for the public.

Use Google Container Engine with a Tomcat container with the Swagger (Open API Specification) framework. Focus on an API for dealers and partners.

Build or leverage an OAuth-compatible access control system.

Build SAML 2.0 SSO compatibility into your authentication system.

Restrict data access based on the source IP address of the partner systems.

Create secondary credentials for each dealer that can be given to the trusted third party.

Create network load balancers. Use preemptible Compute Engine instances.

Create network load balancers. Use non-preemptible Compute Engine instances.

Create a global load balancer with managed instance groups and autoscaling policies. Use preemptible Compute Engine instances.

Create a global load balancer with managed instance groups and autoscaling policies. Use non-preemptible Compute Engine instances.

Store as much analytics and game activity data as financially feasible today so it can be used to train machine learning models to predict user behavior in the future.

Begin packaging their game backend artifacts in container images and running them on Kubernetes Engine to improve the availability to scale up or down based on game activity.

Set up a CI/CD pipeline using Jenkins and Spinnaker to automate canary deployments and improve development velocity.

Adopt a schema versioning tool to reduce downtime when adding new game features that require storing additional player data in the database.

Implement a weekly rolling maintenance process for the Linux virtual machines so they can apply critical kernel patches and package updates and reduce the risk of 0-day vulnerabilities.

Deploy failure injection software to the game analytics platform that can inject additional latency to mobile client analytics traffic.

Build a test client that can be run from a mobile phone emulator on a Compute Engine virtual machine, and run multiple copies in Google Cloud Platform regions all over the world to generate realistic traffic.

Add the ability to introduce a random amount of delay before beginning to process analytics files uploaded from mobile devices.

Create an opt-in beta of the game that runs on players ' mobile devices and collects response times from analytics endpoints running in Google Cloud Platform regions all over the world.

Use Cloud SQL for time series data, and use Cloud Bigtable for historical data queries.

Use Cloud SQL to replace MySQL, and use Cloud Spanner for historical data queries.

Use Cloud Bigtable to replace MySQL, and use BigQuery for historical data queries.

Use Cloud Bigtable for time series data, use Cloud Spanner for transactional data, and use BigQuery for historical data queries.

Create an instance template for the backend. For every region, deploy it on a multi-zone managed instance group. Use an L4 load balancer.

Create an instance template for the backend. For every region, deploy it on a single-zone managed instance group. Use an L4 load balancer.

Create an instance template for the backend. For every region, deploy it on a multi-zone managed instance group. Use an L7 load balancer.

Create an instance template for the backend. For every region, deploy it on a single-zone managed instance group. Use an L7 load balancer.

Delete the virtual machine (VM) and disks and create a new one.

Delete the instance, attach the disk to a new VM, and investigate.

Take a snapshot of the disk and connect to a new machine to investigate.

Check inbound firewall rules for the network the machine is connected to.

Connect the machine to another network with very simple firewall rules and investigate.

Print the Serial Console output for the instance for troubleshooting, activate the interactive console, and investigate.

Use a Cloud Storage bucket in the same region as your virtual machines Configure lifecycle policies to delete files after processing

Use Filestore to store temporary files

Use performance persistent disks.

Use Local SSDs attached to the VMs running the generative Al models

Have your engineers inspect the data for patterns, and then create an algorithm with rules that make operational adjustments automatically.

Capture all operating data, train machine learning models that identify ideal operations, and run locally to make operational adjustments automatically.

Implement a Google Cloud Dataflow streaming job with a sliding window, and use Google Cloud Messaging (GCM) to make operational adjustments automatically.

Capture all operating data, train machine learning models that identify ideal operations, and host in Google Cloud Machine Learning (ML) Platform to make operational adjustments automatically.

Use one Google Container Engine cluster of FTP servers. Save the data to a Multi-Regional bucket. Run the ETL process using data in the bucket.

Use multiple Google Container Engine clusters running FTP servers located in different regions. Save the data to Multi-Regional buckets in us, eu, and asia. Run the ETL process using the data in the bucket.

Directly transfer the files to different Google Cloud Multi-Regional Storage bucket locations in us, eu, and asia using Google APIs over HTTP(S). Run the ETL process using the data in the bucket.

Directly transfer the files to a different Google Cloud Regional Storage bucket location in us, eu, and asia using Google APIs over HTTP(S). Run the ETL process to retrieve the data from each Regional bucket.

Design a migration plan to move all of Cymbal ' s data to Cloud Storage, and use Compute Engine for all business logic

Design a migration plan to move all of Cymbal ' s data to Cloud Storage, and use Cloud Run functions for all business logic

Design a migration plan, starting with a pilot project focusing on a specific product category, and gradually expand to other categories.

Design a migration plan with a scheduled window to move all components at once Perform extensive testing to ensure a successful migration.

Migrate all databases to Spanner for consistency, and use Cloud Storage Standard for image storage

Migrate all databases to self-managed instances on Compute Engino. and use a persistent disk for image storage.

Migrate MySQL and SQL Server to Spanner. Redis to Memorystore. and MongoDB to Firestore Use Cloud Storage Standard for image storage, and move

images to Cloud Storage Nearline storage when products become less popular.

Migrate MySQL to Cloud SQL. SQL Server to Cloud SQL. Redis to Memorystore. and MongoDB to Firestore. Use Cloud Storage Standard for image storage, and move images to Cloud Storage Coldline storage when products become less popular

Use AlloyDB for structured product data, and Cloud Storage for product images

Use Spanner for the structured product data, and BigTable for product images

Use Filestore for the structured product data and Cloud Storage for product images

Use Cloud Storage for structured product data, and BigQuery for product images

Use Cloud SQL to store the customer data and product catalog.

Configure Cloud Build to call Al Applications (formerly Vertex Al Agent Builder).

Deploy a Google Kubernetes Engine (GKE) cluster with autoscaling enabled

Create a single, large Compute Engine VM instance with a high CPU allocation.

Investigate if the project owner disabled a soft-delete policy on the bucket holding the video files.

Investigate if the project owner moved from dual-region storage to region storage

Investigate If the project owner enabled a soft-delete policy on the bucket holding the video files.

Investigate if the project owner moved from multi-region storage to region stotage.

Use a bastion host to provide secure access lo Google Cloud resources from Cymbal ' s on-premises systems.

Configure a static VPN connection using SSH tunnels to connect the on-premises systems to Google Cloud

Configure a Cloud VPN gateway and establish a VPN tunnel Configure firewall rules to restrict access to specific resources and services based on IP addresses and ports.

Use Google Cloud ' s VPC peering to connect Cymbal ' s on-premises network to Google Cloud.

Configure an organizational policy which constrains where resources can be deployed.

Configure IAM conditions to limit what resources can be configured.

Configure the quotas for resources in the regions not being used to 0.

Configure a custom alert in Cloud Monitoring so you can disable resources as they are created in other

regions.

Configure a global load balancer connected to a managed instance group running Compute Engine

instances.

Configure kubemci with a global load balancer and Google Kubernetes Engine.

Configure a global load balancer with Google Kubernetes Engine.

Configure Ingress for Anthos with a global load balancer and Google Kubernetes Engine.

Upload your mobile app to the Firebase Test Lab, and test the mobile app on Android and iOS devices.

Create Android and iOS VMs on Google Cloud, install the mobile app on the VMs, and test the mobile app.

Create Android and iOS containers on Google Kubernetes Engine (GKE), install the mobile app on the

containers, and test the mobile app.

Upload your mobile app with different configurations to Firebase Hosting and test each configuration.

Treat every micro service call between modules on the vehicle as untrusted.

Require IPv6 for connectivity to ensure a secure address space.

Use a trusted platform module (TPM) and verify firmware and binaries on boot.

Use a functional programming language to isolate code execution cycles.

Use multiple connectivity subsystems for redundancy.

Enclose the vehicle ' s drive electronics in a Faraday cage to isolate chips.

Have the vehicle’ computer compress the data in hourly snapshots, and store it in a Google Cloud storage (GCS) Nearline bucket.

Push the telemetry data in Real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery.

Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable.

Have the vehicle ' s computer compress the data in hourly snapshots, a Store it in a GCS Coldline bucket.

Use the Data Transfer appliance to perform an offline migration

Use a commercial partner ETL solution to extract the data from the on-premises database and upload it into Cloud Storage

Develop a Dataflow job to read data directly from the database and write it into Cloud Storage

Compress the data and upload it with gsutii -m to enable multi-threaded copy

Create a Compute engine instance with CPU and Memory options similar to your application’s current on-premises virtual machine. Install the cloud monitoring agent, and deploy the third party application. Run a load with normal traffic levels on third party application and follow the Rightsizing Recommendations in the Cloud Console

Create an App Engine flexible environment, and deploy the third party application using a Docker file and a custom runtime. Set CPU and memory options similar to your application’s current on-premises virtual machine in the app.yaml file.

Create an instance template with the smallest available machine type, and use an image of the third party application taken from the current on-premises virtual machine. Create a managed instance group that uses average CPU to autoscale the number of instances in the group. Modify the average CPU utilization threshold to optimize the number of instances running.

Create multiple Compute Engine instances with varying CPU and memory options. Install the cloud monitoring agent and deploy the third-party application on each of them. Run a load test with high traffic levels on the application and use the results to determine the optimal settings.

Configure a cron job to use the gcloud tool to take regular backups using persistent disk snapshots.

Mount a Local SSD volume as the backup location. After the backup is complete, use gsutil to move the backup to Google Cloud Storage.

Use gcsfuse to mount a Google Cloud Storage bucket as a volume directly on the instance and write backups to the mounted location using mysqldump

Mount additional persistent disk volumes onto each virtual machine (VM) instance in a RAID10 array and use LVM to create snapshots to send to Cloud Storage.

Create Cloud SQL read replicas in regions in Europe and Asia, and direct all database read traffic from those continents to their local replica.

Use a regional external Network Load Balancer in us-central1 to better distribute the incoming global traffic.

Enable Cloud CDN for the backend service that serves the static assets, and configure it as part of a global external HTTP(S) Load Balancer.

Deploy the application frontend service to Compute Engine managed instance groups in regions in Europe and Asia. Use a global external HTTP(S) Load Balancer to route user traffic to the nearest region.

In the Cloud Platform Console, increase the size of the persistent disk and use the resize2fs command in Linux.

Shut down the virtual machine, use the Cloud Platform Console to increase the persistent disk size, then restart the virtual machine.

In the Cloud Platform Console, increase the size of the persistent disk and verify the new space is ready to use with the fdisk command in Linux.

In the Cloud Platform Console, create a new persistent disk attached to the virtual machine, format and mount it, and configure the database service to move the files to the new disk.

In the Cloud Platform Console, create a snapshot of the persistent disk, restore the snapshot to a new larger disk, unmount the old disk, mount the new disk, and restart the database service.

Cloud Deployment Manager uses Python.

Cloud Deployment Manager APIs could be deprecated in the future.

Cloud Deployment Manager is unfamiliar to the company ' s engineers.

Cloud Deployment Manager requires a Google APIs service account to run.

Cloud Deployment Manager can be used to permanently delete cloud resources.

Cloud Deployment Manager only supports automation of Google Cloud resources.

Upload missing JAR files and redeploy your application.

Digitally sign all of your JAR files and redeploy your application

Recompile the CLoakedServlet class using and MD5 hash instead of SHA1