Google Professional-Cloud-Architect Dumps

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.

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

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.

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.

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.

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.

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.

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 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

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 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.

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

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

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 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.

Option A

Option B

Option C

Option D

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.

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.

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.

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.

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

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 scalable environment in GCP for simulating production load.

Use the existing infrastructure to test the GCP-based backend at scale.

Build stress tests into each component of your application using resources internal to GCP to simulate load.

Create a set of static environments in GCP to test different levels of load — for example, high, medium, and low.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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

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 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.

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

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 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.

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”.

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.

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.

Create a BigQuery table for the European data, and set the table retention period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.

Create a BigQuery table for the European data, and set the table retention period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action when with an Age condition of 36 months.

Create a BigQuery time-partitioned table for the European data, and set the partition expiration period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.

Create a BigQuery time-partitioned table for the European data, and set the partition period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action with an Age condition of 36 months.

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

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.

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.

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.

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.

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.

They should enable Google Stackdriver 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 run the end-to-end tests in the cloud staging environment to determine if the code is working as intended.

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

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

Query logs in Cloud Logging.

Analyze the data via Cloud Profiler.

Instrument the code to use Cloud Trace.

Inspect data from Snapshot Debugger.

Set up API key validation.

Integrate OAuth 2.0 authorization.

Configure Quota policies.

Activate XML threat protection.

Run unit testing.

Use load testing.

Perform end-to-end testing.

Execute integration testing.

Set up VPC Service Controls to restrict access to sensitive resources and prevent data exfiltration.

Configure Cloud Next Generation Firewall (NGFW) with custom rules to filter malicious traffic at the network level.

Deploy Google Cloud Armor with pre-configured and custom rules for L3/L4 and L7 protection.

Activate Security Command Center to monitor security posture and detect potential threats.

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.

Configure reserved VM instances

Deploy spot VM instances.

Set up standard VM instances.

Use Cloud Run functions.

Create a Dataproc cluster using standard worker instances.

Create a Dataproc cluster using spot worker instances.

Manually deploy a Hadoop cluster on Compute Engine using standard instances.

Manually deploy a Hadoop cluster on Compute Engine using preemptible instances.

Configure their replication to use UDP.

Configure a Google Cloud Dedicated Interconnect.

Restore their database daily using Google Cloud SQL.

Add additional VPN connections and load balance them.

Send the replicated transaction to Google Cloud Pub/Sub.