Professional-Cloud-Architect 100% Pass Guaranteed Download Google Cloud Certified Exam PDF Q&A [Q153-Q171]

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Professional-Cloud-Architect 100% Pass Guaranteed Download Google Cloud Certified Exam PDF Q&A

Professional-Cloud-Architect Practice Test Dumps with 100% Passing Guarantee

Google Professional-Cloud-Architect certification is highly valued in the industry and is recognized by many organizations as a standard for cloud architects. Google Certified Professional – Cloud Architect (GCP) certification validates the skills of an individual in designing, developing, and managing solutions using Google Cloud technologies. It also demonstrates the candidate’s ability to design solutions that are highly available, scalable, and secure.

NEW QUESTION 153
A small number of API requests to your microservices-based application take a very long time. You know that each request to the API can traverse many services. You want to know which service takes the longest in those cases. What should you do?

Set timeouts on your application so that you can fail requests faster.

Send custom metrics for each of your requests to Stackdriver Monitoring.

Use Stackdriver Monitoring to look for insights that show when your API latencies are high.

Instrument your application with Stackdnver Trace in order to break down the request latencies at each microservice.

NEW QUESTION 154
Your web application must comply with the requirements of the European Union’s General Data Protection Regulation (GDPR). You are responsible for the technical architecture of your web application. What should you do?

Ensure that your web application only uses native features and services of Google Cloud Platform, because Google already has various certifications and provides “pass-on” compliance when you use native features.

Enable the relevant GDPR compliance setting within the GCPConsole for each of the services in use within your application.

Ensure that Cloud Security Scanner is part of your test planning strategy in order to pick up any compliance gaps.

Define a design for the security of data in your web application that meets GDPR requirements.

NEW QUESTION 155
The current Dress4win system architecture has high latency to some customers because it is located in
one data center.
As of a future evaluation and optimizing for performance in the cloud, Dresss4win wants to distribute its
system architecture to multiple locations when Google cloud platform.
Which approach should they use?

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.

NEW QUESTION 156
Your company has an application running on multiple Compute Engine instances. You need to ensure that the application can communicate with an on-premises service that requires high throughput via internal IPs, while minimizing latency. What should you do?

Use OpenVPN to configure a VPN tunnel between the on-premises environment and Google Cloud.

Configure a direct peering connection between the on-premises environment and Google Cloud.

Use Cloud VPN to configure a VPN tunnel between the on-premises environment and Google Cloud.

Configure a Cloud Dedicated Interconnect connection between the on-premises environment and Google Cloud.

NEW QUESTION 157
Your development team has installed a new Linux kernel module on the batch servers in Google Compute Engine (GCE) virtual machines (VMs) to speed up the nightly batch process. Two days after the installation,
50% of the batch servers failed the nightly batch run. You want to collect details on the failure to pass back to the development team.
Which three actions should you take? (Choose three.)

Use Stackdriver Logging to search for the module log entries

Read the debug GCE Activity log using the API or Cloud Console

Use gcloud or Cloud Console to connect to the serial console and observe the logs

Identify whether a live migration event of the failed server occurred, using in the activity log

Adjust the Google Stackdriver timeline to match the failure time, and observe the batch server metrics

Export a debug VM into an image, and run the image on a local server where kernel log messages will be displayed on the native screen

NEW QUESTION 158
Your customer is receiving reports that their recently updated Google App Engine application is taking approximately 30 seconds to load for some of their users. This behavior was not reported before the update. What strategy should you take?

Work with your ISP to diagnose the problem.

Open a support ticket to ask for network capture and flow data to diagnose the problem, then roll back your application.

Roll back to an earlier known good release initially, then use Stackdriver Trace and logging to diagnose the problem in a development/test/staging environment.

Roll back to an earlier known good release, then push the release again at a quieter period to investigate. Then use Stackdriver Trace and logging to diagnose the problem.

NEW QUESTION 159
You need to ensure reliability for your application and operations by supporting reliable task a scheduling for compute on GCP. Leveraging Google best practices, what should you do?

Using the Cron service provided by App Engine, publishing messages directly to a message-processing utility service running on Compute Engine instances.

Using the Cron service provided by App Engine, publish messages to a Cloud Pub/Sub topic. Subscribe to that topic using a message-processing utility service running on Compute Engine instances.

Using the Cron service provided by Google Kubernetes Engine (GKE), publish messages directly to a message-processing utility service running on Compute Engine instances.

Using the Cron service provided by GKE, publish messages to a Cloud Pub/Sub topic. Subscribe to that topic using a message-processing utility service running on Compute Engine instances.

NEW QUESTION 160
The current Dress4win system architecture has high latency to some customers because it is located in one data center.
As of a future evaluation and optimizing for performance in the cloud, Dresss4win wants to distribute its system architecture to multiple locations when Google cloud platform.
Which approach should they use?

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.

NEW QUESTION 161
Dress4Win has configured a new uptime check with Google Stackdriver for several of their legacy
services. The Stackdriver dashboard is not reporting the services as healthy.
What should they do?

Install the Stackdriver agent on all of the legacy web servers.

In the Cloud Platform Console download the list of the uptime servers’ IP addresses and create an
inbound firewall rule

Configure their load balancer to pass through the User-Agent HTTP header when the value matches
GoogleStackdriverMonitoring-UptimeChecks (https://cloud.google.com/monitoring)

Configure their legacy web servers to allow requests that contain user-Agent HTTP header when the
value matches GoogleStackdriverMonitoring-UptimeChecks (https://cloud.google.com/monitoring)

NEW QUESTION 162
For this question, refer to the JencoMart case study
A few days after JencoMart migrates the user credentials database to Google Cloud Platform and shuts down the old server, the new database server stops responding to SSH connections. It is still serving database requests to the application servers correctly. What three steps should you take to diagnose the problem? Choose 3 answers

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.

NEW QUESTION 163
Case Study: 6 – TerramEarth
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.
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 and Windows-based systems that reside in a single U.S. west coast based data center. These systems gzip CSV files from the field and upload via FTP, 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.
* Support the dealer network with more data on how their customers use their equipment to 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.
Technical Requirements
Expand beyond a single datacenter to decrease latency to the American Midwest and east
* coast.
Create a backup strategy.
* Increase security of data transfer from equipment to the datacenter.
* Improve data in the data warehouse.
* Use customer and equipment data to anticipate customer needs.
* Application 1: Data ingest
A custom Python application reads uploaded datafiles from a single server, writes to the data warehouse.
Compute:
Windows Server 2008 R2
* – 16 CPUs
– 128 GB of RAM
– 10 TB local HDD storage
Application 2: Reporting
An off the shelf application that business analysts use to run a daily report to see what equipment needs repair. Only 2 analysts of a team of 10 (5 west coast, 5 east coast) can connect to the reporting application at a time.
Compute:
Off the shelf application. License tied to number of physical CPUs
* – Windows Server 2008 R2
– 16 CPUs
– 32 GB of RAM
– 500 GB HDD
Data warehouse:
A single PostgreSQL server
* – RedHat Linux
– 64 CPUs
– 128 GB of RAM
– 4x 6TB HDD in RAID 0
Executive Statement
Our competitive advantage has always been in the manufacturing process, with our ability to build better vehicles for lower 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. My goals are to build our skills while addressing immediate market needs through incremental innovations.
For this question, refer to the TerramEarth case study. You are asked to design a new architecture for the ingestion of the data of the 200,000 vehicles that are connected to a cellular network. You want to follow Google-recommended practices.
Considering the technical requirements, which components should you use for the ingestion of the data?

Google Kubernetes Engine with an SSL Ingress

Cloud IoT Core with public/private key pairs

Compute Engine with project-wide SSH keys

Compute Engine with specific SSH keys

NEW QUESTION 164
Case Study: 6 – TerramEarth
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.
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 and Windows-based systems that reside in a single U.S. west coast based data center. These systems gzip CSV files from the field and upload via FTP, 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.
* Support the dealer network with more data on how their customers use their equipment to 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.
Technical Requirements
Expand beyond a single datacenter to decrease latency to the American Midwest and east
* coast.
Create a backup strategy.
* Increase security of data transfer from equipment to the datacenter.
* Improve data in the data warehouse.
* Use customer and equipment data to anticipate customer needs.
* Application 1: Data ingest
A custom Python application reads uploaded datafiles from a single server, writes to the data warehouse.
Compute:
Windows Server 2008 R2
* – 16 CPUs
– 128 GB of RAM
– 10 TB local HDD storage
Application 2: Reporting
An off the shelf application that business analysts use to run a daily report to see what equipment needs repair. Only 2 analysts of a team of 10 (5 west coast, 5 east coast) can connect to the reporting application at a time.
Compute:
Off the shelf application. License tied to number of physical CPUs
* – Windows Server 2008 R2
– 16 CPUs
– 32 GB of RAM
– 500 GB HDD
Data warehouse:
A single PostgreSQL server
* – RedHat Linux
– 64 CPUs
– 128 GB of RAM
– 4x 6TB HDD in RAID 0
Executive Statement
Our competitive advantage has always been in the manufacturing process, with our ability to build better vehicles for lower 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. My goals are to build our skills while addressing immediate market needs through incremental innovations.
For this question, refer to the TerramEarth case study. To be compliant with European GDPR regulation, TerramEarth is required to delete data generated from its European customers after a period of 36 months when it contains personal data. In the new architecture, this data will be stored in both Cloud Storage and BigQuery. What should you do?

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.

NEW QUESTION 165
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. TerramEarth’s 20 million vehicles are scattered around the world. Based on the vehicle’s location its telemetry data is stored in a Google Cloud Storage (GCS) regional bucket (US. Europe, or Asia). The CTO has asked you to run a report on the raw telemetry data to determine why vehicles are breaking down after 100 K miles. You want to run this job on all the data. What is the most cost-effective way to run this job?

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 regional bucket and use a Cloud Dataproc cluster to finish the job.

NEW QUESTION 166
You company has a Kubernetes application that pulls messages from Pub/Sub and stores them in Firestore.
Because the application is simple, it was deployed as a single pod. The infrastructure team has analyzed Pub/Sub metrics and discovered that the application cannot process the messages in real time. Most of them wait for minutes before being processed. You need to scale the elaboration process that is I/O-intensive.
What should you do?

Configure a Kubernetes autoscaling based on the subscription/push_request metric.

Use the -enable- autoscaling flag when you create the Kubernetes cluster

Configure a Kubernetes autoscaling based on the subscription/num_undelivered message metric.

Use kubectl autoscale deployment APP_NAME -max 6 -min 2 -cpu- percent 50 to configure Kubernetes autoscaling deployment

NEW QUESTION 167
Your organization wants to control IAM policies for different departments independently, but centrally.
Which approach should you take?

Multiple Organizations with multiple Folders

Multiple Organizations, one for each department

A single Organization with Folders for each department

A single Organization with multiple projects, each with a central owner

NEW QUESTION 168
Your customer is receiving reports that their recently updated Google App Engine application is taking approximately 30 seconds to load for some of their users. This behavior was not reported before the update. What strategy should you take?

Work with your ISP to diagnose the problem.

Open a support ticket to ask for network capture and flow data to diagnose the problem, then roll back your application.

Roll back to an earlier known good release initially, then use Stackdriver Trace and logging to diagnose the problem in a development/test/staging environment.

Roll back to an earlier known good release, then push the release again at a quieter period to investigate. Then use Stackdriver Trace and logging to diagnose the problem.

NEW QUESTION 169
Mountkirk Games wants to set up a real-time analytics platform for their new game. The new platform must
meet their technical requirements.
Which combination of Google technologies will meet all of their requirements?

Kubernetes 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

NEW QUESTION 170
Operational parameters such as oil pressure are adjustable on each of TerramEarth’s vehicles to increase their efficiency, depending on their environmental conditions. Your primary goal is to increase the operating efficiency of all 20 million cellular and unconnected vehicles in the field.
How can you accomplish this goal?

Have you 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

NEW QUESTION 171
For this question, refer to the Mountkirk Games case study.
Mountkirk Games wants you to design their new testing strategy. How should the test coverage differ from their existing backends on the other platforms?

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.

Google Professional-Cloud-Architect certification is a valuable asset for cloud architects, engineers, and consultants who want to demonstrate their expertise in designing and managing cloud-based solutions using Google Cloud Platform. Google Certified Professional – Cloud Architect (GCP) certification not only validates an individual’s skills but also helps them stand out in the job market and opens up new career opportunities.

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