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

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.

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

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.

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.

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.

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.

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.

Deploy Cloud Run services to multiple availability zones. Create Cloud Endpoints that point to the services. Create a global HTIP(S) Load Balancing instance and attach the Cloud Endpoints to its backend.

Deploy Cloud Run services to multiple regions Create serverless network endpoint groups pointing to the services. Add the serverless NE Gs to a backend service that is used by a global HTIP(S) Load Balancing instance.

Cloud Run services to multiple regions. In Cloud DNS, create a latency-based DNS name that points to the services.

Deploy Cloud Run services to multiple availability zones. Create a TCP/IP global load balancer. Add the Cloud Run Endpoints to its backend service.

Use Explainable AI.

Use Vision AI.

Use Google Cloud’s operations suite.

Use Jupyter Notebooks.

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.

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.

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.

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.

Query logs in Cloud Logging.

Analyze the data via Cloud Profiler.

Instrument the code to use Cloud Trace.

Inspect data from Snapshot Debugger.

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.

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.

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.

Cloud Bigtable

Cloud Spanner

BigQuery

Cloud Datastore

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.

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.

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.

Opex/capex allocation, LAN changes, capacity planning

Capacity planning, TCO calculations, opex/capex allocation

Capacity planning, utilization measurement, data center expansion

Data Center expansion, TCO calculations, utilization measurement

Option A

Option B

Option C

Option D

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.

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 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.

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.

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.

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.

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.

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.

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

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.

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.

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

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.

Cloud Spanner

Google BigQuery

Google Cloud SQL

Google Cloud Datastore

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.

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.

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

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.

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.

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.

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 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 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.

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

Bucket Lock

Object Versioning

Object change notification

Object Lifecycle Management

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.

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.

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.

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.

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.

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.