Professional-Cloud-Architect Google Certified Professional - Cloud Architect (GCP) Questions and Answers

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

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.

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.

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.

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.

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.

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.

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.

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.

Cloud Bigtable

Cloud Spanner

BigQuery

Cloud Datastore

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.

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.

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.

Implement a Classic VPN connection.

Implement an HA VPN connection.

Configure a Partner Interconnect connection.

Provision a Dedicated Interconnect connection.

Use separate VPCs to restrict traffic

Use firewall rules based on network tags attached to the compute instances

Use Cloud DNS and only allow connections from authorized hostnames

Use service accounts and configure the web application particular service accounts to have access

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.

~/bin

Cloud Storage

/google/scripts

/usr/local/bin

Direct them to download and install the Google StackDriver logging agent.

Send them a list of online resources about logging best practices.

Help them define their requirements and assess viable logging tools.

Help them upgrade their current tool to take advantage of any new features.

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”.

Google Kubernetes Engine with an SSL Ingress

Cloud IoT Core with public/private key pairs

Compute Engine with project-wide SSH keys

Compute Engine with specific SSH keys

Package your application into a Docker image, and deploy it to Kubernetes on Compute Engine.

Leverage Cloud Build to create a container image, and deploy it automatically to Kubernetes on Compute Engine.

Assess application and dependencies for containerization. Develop a migration strategy for deployment to GKE in Standard mode.

Assess application and dependencies for containerization. Develop a migration strategy for deployment to GKE in Autopilot mode.

Use the gsutil command line tool to download the data to your VM instances.

Copy the data from Cloud Storage to Filestore, and then mount the Filestore volume as a local file system on your VM instances.

Create a shared persistent disk, attach the disk to your VM instances, and load data from the Cloud Storage bucket.

Use the gcsfuse command line tool to mount the Cloud Storage bucket as a local file system, and perform read/write operations in your bucket using standard file system semantics.

App Engine is the only compute platform on GCP that is certified for PCI DSS hosting.

Kubernetes Engine cannot be used under PCI DSS because it is considered shared hosting.

Kubernetes Engine and GCP provide the tools you need to build a PCI DSS-compliant environment.

All Google Cloud services are usable because Google Cloud Platform is certified PCI-compliant.

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 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.

Logging, Alerts, Insights, Debug

Monitoring, Trace, Debug, Logging

Monitoring, Logging, Alerts, Error Reporting

Monitoring, Logging, Debug, Error Report

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.

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.

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.

Store image files in a Google Cloud Storage bucket. Use Google Cloud Datastore to maintain metadata that maps each customer ' s ID and their image files.

Store image files in a Google Cloud Storage bucket. Add custom metadata to the uploaded images in Cloud Storage that contains the customer ' s unique ID.

Use a distributed file system to store customers ' images. As storage needs increase, add more persistent disks and/or nodes. Assign each customer a unique ID, which sets each file ' s owner attribute, ensuring privacy of images.

Use a distributed file system to store customers ' images. As storage needs increase, add more persistent disks and/or nodes. Use a Google Cloud SQL database to maintain metadata that maps each customer ' s ID to their image files.

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

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.

Create a scheduled job in Cloud Run to invoke a container every minute. The container will check the application URL If the application is down, switch the URL to the " Site is unavailable " page, and notify the Ops team.

Create a cron job on a Compute Engine VM that runs every minute. The cron job invokes a Python program to check the application URL If the application is down, switch the URL to the " Site is unavailable " page, and notify the Ops team.

Create a Cloud Monitoring uptime check to validate the application URL If it fails, put a message in a Pub/Sub queue that triggers a Cloud Function to switch the URL to the " Site is unavailable " page, and notify the Ops team.

Use Cloud Error Reporting to check the application URL If the application is down, switch the URL to the " Site is unavailable " page, and notify the Ops team.

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

Replace the existing data warehouse with BigQuery. Use table partitioning.

Replace the existing data warehouse with a Compute Engine instance with 96 CPUs.

Replace the existing data warehouse with BigQuery. Use federated data sources.

Replace the existing data warehouse with a Compute Engine instance with 96 CPUs. Add an additional Compute Engine pre-emptible instance with 32 CPUs.

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.

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.

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.

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.

Set up Cloud Tasks and a Cloud Storage bucket that triggers a Cloud Function.

Set up a Cloud Logging sink and a Cloud Storage bucket that triggers a Cloud Function.

Configure the deployment job to notify a Pub/Sub queue that triggers a Cloud Function.

Set up Identity and Access Management (IAM) and Confidential Computing to trigger a Cloud Function.

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.

Store the card data in Secret Manager after running a query to identify duplicates.

Encrypt the card data with a deterministic algorithm stored in Firestore using Datastore mode.

Encrypt the card data with a deterministic algorithm and shard it across multiple Memorystore instances.

Use column-level encryption to store the data in Cloud SQL.

Apply a Cloud Armor security policy to external load balancers using a named IP list for Fastly.

Apply a Cloud Armor security policy to external load balancers using the IP addresses that Fastly has published. C. Apply a VPC firewall rule on port 443 for Fastly IP address ranges.

Apply a VPC firewall rule on port 443 for network resources tagged with scurceiplisr-fasrly.

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 Explainable AI.

Use Vision AI.

Use Google Cloud’s operations suite.

Use Jupyter Notebooks.

Log into each Compute Engine instance and collect disk, CPU, memory, and network usage statistics for

analysis.

Use the gcloud compute instances list to list the virtual machine instances that have the idle: true label set.

Use the gcloud recommender command to list the idle virtual machine instances.

From the Google Console, identify which Compute Engine instances in the managed instance groups are

no longer responding to health check probes.

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

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.

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.

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 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.

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 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

Set up API key validation.

Integrate OAuth 2.0 authorization.

Configure Quota policies.

Activate XML threat protection.

Cloud Spanner

Google BigQuery

Google Cloud SQL

Google Cloud Datastore

Configure Cloud Storage to use server-side encryption with Google-managed encryption keys. Create a bucket policy to restrict access to only authorized Google groups and required service accounts.

Use Cloud Storage default encryption at rest. Implement fine-grained access control using IAM roles and groups to restrict access to sensitive buckets.

Implement client-side encryption before uploading it to Cloud Storage. Store the encryption keys in a HashiCorp Vault instance deployed on Google Kubernetes Engine (GKE). Implement fine-grained access control to sensitive Cloud Storage buckets using IAM roles.

Use customer-managed encryption keys (CMEK) for all Cloud Storage buckets storing sensitive media content. Implement fine-grained access control using IAM roles and groups to restrict access to sensitive buckets.

Provision service account keys for the on-premises infrastructure and for the GCE virtual machines (VMs).

Authenticate the on-premises infrastructure with a user account and provision service account keys for the VMs.

Provision service account keys for the on-premises infrastructure and use Google Cloud Platform (GCP) managed keys for the VMs

Deploy a custom authentication service on GCE/Google Container Engine (GKE) for the on-premises infrastructure and use GCP managed keys for the VMs.

Create two G Suite accounts to manage users: one for development/test/staging and one for production. Each account should contain one project for every application.

Create two G Suite accounts to manage users: one with a single project for all development applications and one with a single project for all production applications.

Create a single G Suite account to manage users with each stage of each application in its own project.

Create a single G Suite account to manage users with one project for the development/test/staging environment and one project for the production environment.

Error rates for requests from Asia

Latency difference between US and Asia

Total visits, error rates, and latency from Asia

Total visits and average latency for users in Asia

The number of character sets present in the database

A single VPN tunnel, which limits throughput

A tier of Google Cloud Storage that is not suited for this task

A copy command that is not suited to operate over long distances

Fewer virtual machines (VMs) in GCP than on-premises machines

A separate storage layer outside the VMs, which is not suited for this task

Complicated internet connectivity between the on-premises infrastructure and GCP

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.