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

Option A

Option B

Option C

Option D

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.

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.

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.

Cloud Spanner

Google BigQuery

Google Cloud SQL

Google Cloud Datastore

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.

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

Write a lifecycle management rule in XML and push it to the bucket with gsutil.

Write a lifecycle management rule in JSON and push it to the bucket with gsutil.

Schedule a cron script using gsutil is -lr gs://backups/** to find and remove items older than 90 days.

Schedule a cron script using gsutil ls -1 gs://backups/** to find and remove items older than 90 days and schedule it with cron.

Create a snapshot schedule for the disk containing the application data. Whenever a zonal outage occurs, use the latest snapshot to restore the disk in the same zone.

Configure the Compute Engine instances with an instance template for the application, and use a regional persistent disk for the application data. Whenever a zonal outage occurs, use the instance template to spin up the application in another zone in the same region. Use the regional persistent disk for the application data.

Create a snapshot schedule for the disk containing the application data. Whenever a zonal outage occurs, use the latest snapshot to restore the disk in another zone within the same region.

Configure the Compute Engine instances with an instance template for the application, and use a regional persistent disk for the application data. Whenever a zonal outage occurs, use the instance template to spin up the application in another region. Use the regional persistent disk for the application data,

Bucket Lock

Object Versioning

Object change notification

Object Lifecycle Management

Create a snapshot of the existing disk. Create an instance template from the snapshot. Create an

autoscaled managed instance group from the instance template.

Create a snapshot of the existing disk. Create a custom image from the snapshot. Create an autoscaled managed instance group from the custom image.

Create a custom image from the existing disk. Create an instance template from the custom image. Create an autoscaled managed instance group from the instance template.

Create an instance template from the existing disk. Create a custom image from the instance template.

Create an autoscaled managed instance group from the custom image.

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.

Create an egress rule with priority 1000 to deny all traffic for all instances. Create another egress rule with priority 100 to allow the Active Directory traffic for all instances.

Create an egress rule with priority 100 to deny all traffic for all instances. Create another egress rule with priority 1000 to allow the Active Directory traffic for all instances.

Create an egress rule with priority 1000 to allow the Active Directory traffic. Rely on the implied deny

egress rule with priority 100 to block all traffic for all instances.

Create an egress rule with priority 100 to allow the Active Directory traffic. Rely on the implied deny egress rule with priority 1000 to block all traffic for all instances.

Cloud Run and BigQuery

Cloud Run and Cloud Bigtable

A Compute Engine autoscaling managed instance group and BigQuery

A Compute Engine autoscaling managed instance group and Cloud Bigtable

Perform the following:

1) Create a managed instance group with f1-micro type machines.

2) Use a startup script to clone the repository, check out the production branch, install the dependencies, and start the Python app.

3) Restart the instances to automatically deploy new production releases.

Perform the following:

1) Create a managed instance group with n1-standard-1 type machines.

2) Build a Compute Engine image from the production branch that contains all of the dependencies and

automatically starts the Python app.

3) Rebuild the Compute Engine image, and update the instance template to deploy new production

releases.

Perform the following:

1) Create a Kubernetes Engine cluster with n1-standard-1 type machines.

2) Build a Docker image from the production branch with all of the dependencies, and tag it with the

version number.

3) Create a Kubernetes Deployment with the imagePullPolicy set to “IfNotPresent” in the staging

namespace, and then promote it to the production namespace after testing.

Perform the following:

1) Create a Kubernetes Engine (GKE) cluster with n1-standard-4 type machines.

2) Build a Docker image from the master branch will all of the dependencies, and tag it with “latest”.

3) Create a Kubernetes Deployment in the default namespace with the imagePullPolicy set to “Always”.

Restart the pods to automatically deploy new production releases.

Create a Compute Engine instance template using the most recent Debian image. Create an instance from this template, and install and configure the application as part of the startup script. Repeat this process whenever a new Google-managed Debian image becomes available.

Create a Debian-based Compute Engine instance, install and configure the application, and use OS patch management to install available updates.

Create an instance with the latest available Debian image. Connect to the instance via SSH, and install and configure the application on the instance. Repeat this process whenever a new Google-managed Debian image becomes available.

Create a Docker container with Debian as the base image. Install and configure the application as part of the Docker image creation process. Host the container on Google Kubernetes Engine and restart the container whenever a new update is available.

1. Define a migration plan based on the list of the applications and their dependencies.

2. Migrate all virtual machines into Compute Engine individually with Migrate for Compute Engine.

1. Perform an assessment of virtual machines running in the current VMware environment.

2.Create images of all disks. Import disks on Compute Engine.

3.Create standard virtual machines where the boot disks are the ones you have imported.

1. Perform an assessment of virtual machines running in the current VMware environment.

2. Define a migration plan, prepare a Migrate for Compute Engine migration RunBook, and execute the migration.

1. Perform an assessment of virtual machines running in the current VMware environment.

2.Install a third-party agent on all selected virtual machines.

3.Migrate all virtual machines into Compute Engine.

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.

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.

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

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.

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.

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.

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.

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 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

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.

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.

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.

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.

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.

Verify that the database is online.

Verify that the project quota hasn't been exceeded.

Verify that the new feature code did not introduce any performance bugs.

Verify that the load-testing team is not running their tool against production.

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.

Google Cloud Storage, Google Cloud Dataflow, Google Compute Engine

Google Cloud Storage, Google App Engine, Google Network Load Balancer

Google Kubernetes Registry, Google Container Engine, Google HTTP(S) Load Balancer

Google Cloud Functions, Google Cloud Pub/Sub, Google Cloud Deployment Manager

Container Engine, Cloud Pub/Sub, and Cloud SQL

Cloud Dataflow, Cloud Storage, Cloud Pub/Sub, and BigQuery

Cloud SQL, Cloud Storage, Cloud Pub/Sub, and Cloud Dataflow

Cloud Dataproc, Cloud Pub/Sub, Cloud SQL, and Cloud Dataflow

Cloud Pub/Sub, Compute Engine, Cloud Storage, and Cloud Dataproc

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.

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.

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.

Vehicles write data directly to GCS.

Vehicles write data directly to Google Cloud Pub/Sub.

Vehicles stream data directly to Google BigQuery.

Vehicles continue to write data using the existing system (FTP).

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.

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

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.

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

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.

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.

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”.

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

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.

glouc compute firewall rules update hlr-policy \

--priority 1000 \

target tags-sourceiplist fastly \

--allow tcp:443

gcloud compute security policies rules update 1000 \

--security-policy hlr-policy \

--expression "evaluatePreconfiguredExpr('sourceiplist-fastly')" \

--action " allow"

gcloud compute firewall rules update

sourceiplist-fastly \

priority 1000 \

allow tcp: 443

gcloud compute priority-policies rules update

1000 \

security policy from fastly

--src- ip-ranges"

-- action " allow"

Use Explainable AI.

Use Vision AI.

Use Google Cloud’s operations suite.

Use Jupyter Notebooks.

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.

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 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.

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.