Google Professional-Cloud-Architect Dumps

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

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.

Cloud Spanner

Google BigQuery

Google Cloud SQL

Google Cloud Datastore

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.

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.

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.

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.

Option A

Option B

Option C

Option D

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

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.

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.

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.

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).

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

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

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

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

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

Build or leverage an OAuth-compatible access control system.

Build SAML 2.0 SSO compatibility into your authentication system.

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

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

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

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.

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.

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.

Increase the Pub/Sub Total Timeout retry value.

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

Turn off Pub/Sub message batching.

Create a backup Pub/Sub message queue.

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

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

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

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

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.

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.

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.

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.

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.

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.

Replace RabbitMQ with Google Pub/Sub.

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

Resize compute resources to match predefined Compute Engine machine types.

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

Use Stackdriver Trace to create a trace list analysis.

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

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

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

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 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 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.

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.

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.

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"

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.

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.

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 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.

Upload missing JAR files and redeploy your application.

Digitally sign all of your JAR files and redeploy your application

Recompile the CLoakedServlet class using and MD5 hash instead of SHA1

Use the Horizontal Pod Autoscaler and enable cluster autoscaling. Use an Ingress resource to loadbalance the HTTPS traffic.

Use the Horizontal Pod Autoscaler and enable cluster autoscaling on the Kubernetes cluster. Use a

Service resource of type LoadBalancer to load-balance the HTTPS traffic.

Enable autoscaling on the Compute Engine instance group. Use an Ingress resource to load balance the HTTPS traffic.

Enable autoscaling on the Compute Engine instance group. Use a Service resource of type LoadBalancer to load-balance the HTTPS traffic.

• Append metadata to file body.

• Compress individual files.

• Name files with serverName-Timestamp.

• Create a new bucket if bucket is older than 1 hour and save individual files to the new bucket. Otherwise, save files to existing bucket

• Batch every 10,000 events with a single manifest file for metadata.

• Compress event files and manifest file into a single archive file.

• Name files using serverName-EventSequence.

• Create a new bucket if bucket is older than 1 day and save the single archive file to the new bucket. Otherwise, save the single archive file to existing bucket.

• Compress individual files.

• Name files with serverName-EventSequence.

• Save files to one bucket

• Set custom metadata headers for each object after saving.

• Append metadata to file body.

• Compress individual files.

• Name files with a random prefix pattern.

• Save files to one bucket

Use the Data Transfer appliance to perform an offline migration

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

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

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

1) Enable automatic storage increase for the instance.

2) Create a Stackdriver alert when CPU usage exceeds 75%, and change the instance type to reduce

CPU usage.

3) Create a Stackdriver alert for replication lag, and shard the database to reduce replication time.

1) Enable automatic storage increase for the instance.

2) Change the instance type to a 32-core machine type to keep CPU usage below 75%.

3) Create a Stackdriver alert for replication lag, and shard the database to reduce replication time.

1) Create a Stackdriver alert when storage exceeds 75%, and increase the available storage on the

instance to create more space.

2) Deploy memcached to reduce CPU load.

3) Change the instance type to a 32-core machine type to reduce replication lag.

1) Create a Stackdriver alert when storage exceeds 75%, and increase the available storage on the

instance to create more space.

2) Deploy memcached to reduce CPU load.

3) Create a Stackdriver alert for replication lag, and change the instance type to a 32-core machine type to reduce replication lag.

A Classic Cloud VPN gateway connected with one tunnel to an on-premises VPN gateway.

A Classic Cloud VPN gateway connected with two tunnels to an on-premises VPN gateway.

An HA Cloud VPN gateway connected with two tunnels to an on-premises VPN gateway.

Two HA Cloud VPN gateways connected to two on-premises VPN gateways. Configure each HA Cloud

VPN gateway to have two tunnels, each connected to different on-premises VPN gateways.

Set up a filter in Cloud Logging and a topic in Pub/Sub to publish the logs

Set up a Cloud Logging Dashboard titled Cloud VPN Logs, and then add a chart that queries for the VPN metrics over a one-year time period

Enable the Compute Engine API and then enable logging on the firewall rules that match the traffic you want to save

Set up a filter in Cloud Logging and a Cloud Storage bucket as an export target for the logs you want to save

Create a cross-region load balancer with URL Maps.

Create an HTTPS load balancer with URL maps.

Create appropriate instance groups and instances. Configure SSL proxy load balancing.

Create a global forwarding rule. Configure SSL proxy balancing.

Add additional nodes to your Container Engine cluster using the following command:

gcloud container clusters resize CLUSTER_NAME --size 10

Add a tag to the instances in the cluster with the following command:

gcloud compute instances add-tags INSTANCE --tags enable --autoscaling max-nodes-10

Update the existing Container Engine cluster with the following command:

gcloud alpha container clusters update mycluster --enable-autoscaling --min-nodes=1 --max-nodes=10

Create a new Container Engine cluster with the following command:

gcloud alpha container clusters create mycluster --enable-autocaling --min-nodes=1 --max-nodes=10

and redeploy your application.