2023 Latest Professional-Cloud-Architect dumps – Instant Download PDF [Q115-Q132]

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2023 Latest Professional-Cloud-Architect dumps – Instant Download PDF

Updated Verified Professional-Cloud-Architect Downloadable Printable Exam Dumps

Google Professional-Cloud-Architect exam is designed to test the skills and knowledge of individuals who work in cloud architecture. Professional-Cloud-Architect exam is part of the Google Certified Professional – Cloud Architect (GCP) certification, which is recognized as a valuable qualification in the cloud computing industry. Professional-Cloud-Architect exam is designed to evaluate the candidate’s ability to design and implement cloud solutions that meet the needs of businesses and organizations.

The Google Professional-Cloud-Architect exam consists of multiple-choice and multiple-select questions that assess the candidate’s ability to design and plan a cloud solution architecture, manage and provision the GCP infrastructure, and optimize the technical and business processes. Professional-Cloud-Architect exam also tests the candidate’s knowledge of security and compliance issues related to GCP.

NO.115 Case Study: 2 – TerramEarth Case Study
Company Overview
TerramEarth manufactures heavy equipment for the mining and agricultural industries: About
80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive.
Company Background
TerramEarth formed in 1946, when several small, family owned companies combined to retool after World War II. The company cares about their employees and customers and considers them to be extended members of their family.
TerramEarth is proud of their ability to innovate on their core products and find new markets as their customers’ needs change. For the past 20 years trends in the industry have been largely toward increasing productivity by using larger vehicles with a human operator.
Solution Concept
There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second.
Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced.
The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules.
Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second, with 22 hours of operation per day.
TerramEarth collects a total of about 9 TB/day from these connected vehicles.
Existing Technical Environment

TerramEarth’s existing architecture is composed of Linux-based systems that reside in a data center. These systems gzip CSV files from the field and upload via FTP, transform and aggregate them, and place the data in their data warehouse. Because this process takes time, aggregated reports are based on data that is 3 weeks old.
With this data, TerramEarth has been able to preemptively stock replacement parts and reduce unplanned downtime of their vehicles by 60%. However, because the data is stale, some customers are without their vehicles for up to 4 weeks while they wait for replacement parts.
Business Requirements
– Decrease unplanned vehicle downtime to less than 1 week, without
increasing the cost of carrying surplus inventory
– Support the dealer network with more data on how their customers use
their equipment IP better position new products and services.
– Have the ability to partner with different companies-especially with
seed and fertilizer suppliers in the fast-growing agricultural
business-to create compelling joint offerings for their customers
CEO Statement
We have been successful in capitalizing on the trend toward larger vehicles to increase the productivity of our customers. Technological change is occurring rapidly and TerramEarth has taken advantage of connected devices technology to provide our customers with better services, such as our intelligent farming equipment. With this technology, we have been able to increase farmers’ yields by 25%, by using past trends to adjust how our vehicles operate. These advances have led to the rapid growth of our agricultural product line, which we expect will generate 50% of our revenues by 2020.
CTO Statement
Our competitive advantage has always been in the manufacturing process with our ability to build better vehicles for tower cost than our competitors. However, new products with different approaches are constantly being developed, and I’m concerned that we lack the skills to undergo the next wave of transformations in our industry. Unfortunately, our CEO doesn’t take technology obsolescence seriously and he considers the many new companies in our industry to be niche players. My goals are to build our skills while addressing immediate market needs through incremental innovations.
For this question refer to the TerramEarth case study.
Which of TerramEarth’s legacy enterprise processes will experience significant change as a result of increased Google Cloud Platform adoption.

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

NO.116 You are using Cloud SQL as the database backend for a large CRM deployment. You want to scale as usage increases and ensure that you don’t run out of storage, maintain 75% CPU usage cores, and keep replication lag below 60 seconds. What are the correct steps to meet your requirements?

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 deploy memcache to reduce load on the master.

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.

NO.117 For this question, refer to the Mountkirk Games case study
Mountkirk Games needs to create a repeatable and configurable mechanism for deploying isolated application environments. Developers and testers can access each other’s environments and resources, but they cannot access staging or production resources. The staging environment needs access to some services from production.
What should you do to isolate development environments from staging and 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.

NO.118 For this question, refer to the Mountkirk Games case study. You need to analyze and define the technical architecture for the database workloads for your company, Mountkirk Games. Considering the business and technical requirements, what should you do?

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.

https://cloud.google.com/bigtable/docs/schema-design-time-series
Topic 9, Mountkrik Games Case 3
Company Overview
Mountkirk Games makes online, session-based, multiplayer games for mobile platforms. They have recently started expanding to other platforms after successfully migrating their on-premises environments to Google Cloud.
Their most recent endeavor is to create a retro-style first-person shooter (FPS) game that allows hundreds of simultaneous players to join a geo-specific digital arena from multiple platforms and locations. A real-time digital banner will display a global leaderboard of all the top players across every active arena.
Solution concept
Mountkirk Games is building a new multiplayer game that they expect to be very popular. They plan to deploy the game’s backend on Google Kubernetes Engine so they can scale rapidly and use Google’s global load balancer to route players to the closest regional game arenas. In order to keep the global leader board in sync, they plan to use a multi-region Spanner cluster.
Existing technical environment
The existing environment was recently migrated to Google Cloud, and five games came across using lift-and-shift virtual machine migrations, with a few minor exceptions. Each new game exists in an isolated Google Cloud project nested below a folder that maintains most of the permissions and network policies. Legacy games with low traffic have been consolidated into a single project. There are also separate environments for development and testing.
Business Requirements
Support multiple gaming platforms.
Support multiple regions.
Support rapid iteration of game features.
Minimize latency.
Optimize for dynamic scaling.
Use managed services and pooled resources.
Minimize costs.
Technical Requirements
Dynamically scale based on game activity.
Publish scoring data on a near real-time global leaderboard.
Store game activity logs in structured files for future analysis.
Use GPU processing to render graphics server-side for multi-platform support.
Support eventual migration of legacy games to this new platform.
Executive statement
Our last game was the first time we used Google Cloud, and it was a tremendous success. We were able to analyze player behavior and game telemetry in ways that we never could before. This success allowed us to bet on a full migration to the cloud and to start building all-new games using cloud-native design principles. Our new game is our most ambitious to date and will open up doors for us to support more gaming platforms beyond mobile. Latency is our top priority, although cost management is the next most important challenge. As with our first cloud-based game, we have grown to expect the cloud to enable advanced analytics capabilities so we can rapidly iterate on our deployments of bug fixes and new functionality.

NO.119 Your company has multiple on-premises systems that serve as sources for reporting. The data has not been maintained well and has become degraded over time. You want to use Google- recommended practices to detect anomalies in your company data. What should you do?

Upload your files into Cloud Storage. Use Cloud Datalab to explore and clean your data.

Upload your files into Cloud Storage. Use Cloud Dataprep to explore and clean your data.

Connect Cloud Datalab to your on-premises systems. Use Cloud Datalab to explore and clean your data.

Connect Cloud Dataprep to your on-premises systems. Use Cloud Dataprep to explore and clean your data.

NO.120 Your company uses the Firewall Insights feature in the Google Network Intelligence Center. You have several firewall rules applied to Compute Engine instances. You need to evaluate the efficiency of the applied firewall ruleset. When you bring up the Firewall Insights page in the Google Cloud Console, you notice that there are no log rows to display. What should you do to troubleshoot the issue?

Enable Virtual Private Cloud (VPC) flow logging.

Enable Firewall Rules Logging for the firewall rules you want to monitor.

Verify that your user account is assigned the compute.networkAdmin Identity and Access Management (IAM) role.

Install the Google Cloud SDK, and verify that there are no Firewall logs in the command line output.

NO.121 You need to migrate Hadoop jobs for your company’s Data Science team without modifying the underlying infrastructure. You want to minimize costs and infrastructure management effort. What should you do?

Create a Dataproc cluster using standard worker instances.

Create a Dataproc cluster using preemptible worker instances.

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

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

NO.122 For this question, refer to the TerramEarth case study. A new architecture that writes all incoming data to BigQuery has been introduced. You notice that the data is dirty, and want to ensure data quality on an automated daily basis while managing cost.
What should you do?

Set up a streaming Cloud Dataflow job, receiving data by the ingestion process. Clean the data in a Cloud Dataflow pipeline.

Create a Cloud Function that reads data from BigQuery and cleans it. Trigger it. Trigger the Cloud Function from a Compute Engine instance.

Create a SQL statement on the data in BigQuery, and save it as a view. Run the view daily, and save the result to a new table.

Use Cloud Dataprep and configure the BigQuery tables as the source. Schedule a daily job to clean the data.

NO.123 Your marketing department wants to send out a promotional email campaign. The development team wants to minimize direct operation management. They project a wide range of possible customer responses, from 100 to 500,000 click-through per day. The link leads to a simple website that explains the promotion and collects user information and preferences.
Which infrastructure should you recommend? Choose 2 answers.

Use Google App Engine to serve the website and Google Cloud Datastore to store user data.

Use a Google Container Engine cluster to serve the website and store data to persistent disk.

Use a managed instance group to serve the website and Google Cloud Bigtable to store user data.

Use a single compute Engine virtual machine (VM) to host a web server, backed by Google Cloud SQL.

NO.124 You are designing an application for use only during business hours. For the minimum viable product release, you’d like to use a managed product that automatically “scales to zero” so you don’t incur costs when there is no activity.
Which primary compute resource should you choose?

Cloud Functions

Compute Engine

Kubernetes Engine

AppEngine flexible environment

NO.125 You are creating a solution to remove backup files older than 90 days from your backup Cloud Storage bucket.
You want to optimize ongoing Cloud Storage spend.
What should you do?

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 ls -lr gs://backups/**to find and remove items older than 90 days

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

NO.126 You have an application deployed on Kubernetes Engine using a Deployment named echo- deployment. The deployment is exposed using a Service called echo-service. You need to perform an update to the application with minimal downtime to the application. What should you do?

Use kubectl set image deployment/echo-deployment <new-image>

Use the rolling update functionality of the Instance Group behind the Kubernetes cluster

Update the deployment yaml file with the new container image. Use kubectl delete deployment/echo-deployment and kubectl create -f <yaml-file>

Update the service yaml file which the new container image. Use kubectl delete service/ echo- service and kubectl create -f <yaml-file>

NO.127 Your application needs to process credit card transactions. You want the smallest scope of Payment Card Industry (PCI) compliance without compromising the ability to analyze transactional data and trends relating to which payment methods are used. How should you design your architecture?

Create a tokenizer service and store only tokenized data.

Create separate projects that only process credit card data.

Create separate subnetworks and isolate the components that process credit card data.

Streamline the audit discovery phase by labeling all of the virtual machines (VMs) that process PCI data.

Enable Logging export to Google BigQuery and use ACLs and views to scope the data shared with the auditor.

NO.128 Your company is moving 75 TB of data into Google Cloud. You want to use Cloud Storage and follow Google- recommended practices. What should you do?

Move your data onto a Transfer Appliance. Use a Transfer Appliance Rehydrator to decrypt the data into Cloud Storage.

Move your data onto a Transfer Appliance. Use Cloud Dataprep to decrypt the data into Cloud Storage.

Install gsutil on each server that contains data. Use resumable transfers to upload the data into Cloud Storage.

Install gsutil on each server containing data. Use streaming transfers to upload the data into Cloud Storage.

NO.129 Your company’s test suite is a custom C++ application that runs tests throughout each day on Linux virtual machines. The full test suite takes several hours to complete, running on a limited number of on premises servers reserved for testing. Your company wants to move the testing infrastructure to the cloud, to reduce the amount of time it takes to fully test a change to the system, while changing the tests as little as possible. Which cloud infrastructure should you recommend?

Google Compute Engine unmanaged instance groups and Network Load Balancer

Google Compute Engine managed instance groups with auto-scaling

Google Cloud Dataproc to run Apache Hadoop jobs to process each test

Google App Engine with Google Stackdriver for logging

NO.130 Your company is running a stateless application on a Compute Engine instance. The application is used heavily during regular business hours and lightly outside of business hours. Users are reporting that the application is slow during peak hours. You need to optimize the application’s performance. What should you do?

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.

NO.131 TerramEarth plans to connect all 20 million vehicles in the field to the cloud. This increases the volume to
20 million 600 byte records a second for 40 TB an hour.
How should you design the data ingestion?

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)

NO.132 You want to ensure Dress4Win’s sales and tax records remain available for infrequent viewing by auditors
for at least 10 years.
Cost optimization is your top priority.
Which cloud services should you choose?

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.