Sample Questions of Professional-Cloud-Architect Dumps With 100% Exam Passing Guarantee [Q160-Q182]

Rate this post

Sample Questions of Professional-Cloud-Architect Dumps With 100% Exam Passing Guarantee

Pass Key features of Professional-Cloud-Architect Course with Updated 270 Questions

Before attending the exam for the Google Professional Cloud Architect certification, the individuals need to develop a good comprehension of its topics. The syllabus of the test is divided into five sections, each including several subtopics. The detailed outline of the exam domains can be viewed on the vendor’s website. A brief overview of the content is provided below:

Implementation Management
The aim of this module is to evaluate the ability of the individuals to advise operation or development team(s) to ensure the successful deployment of the solution. This includes application development, API best practices, testing frameworks, as well as data & system migration and management tooling. The examinees should also be capable of interacting with Google Cloud programmatically. This requires their understanding of Google Cloud Shell, Google Cloud SDK, and Cloud Emulators.
Cloud Solution Architecture Design & Planning
Here the candidates need to demonstrate their skills in designing a solution infrastructure that satisfies the business needs; envisioning future solution improvements; creating a migration plan; designing network, compute, and storage resources; designing a solution infrastructure that satisfies the technical needs.
Ensuring of Solution & Operations Reliability
To tackle the questions related to the last domain, the applicants are required to be conversant with monitoring/logging/profiling/alerting solutions. They should know how to perform deployment & release management as well as evaluate quality control measures. IN addition, the specialists must be able to assist with the support of deployed solutions.
Management & Provisioning of Solution Infrastructure
In the framework of this topic, the test takers will be asked to showcase their ability to configure network topologies, individual storage systems, as well as compute systems.
Analysis & Optimization of Technical & Business Processes
Within this subject area, the students need to demonstrate their proficiency in analyzing and determining technical as well as business processes. It also requires their skills in developing procedures to guarantee the reliability of solutions in production.

During the exam for the Google Professional Cloud Architect certification, some of the questions will refer you to a case study describing a fictitious business & solution concept. These case studies are designed to provide the students with additional context to help them choose the right answer(s). The candidates can review the examples of possible case studies in the official guide.

The Professional-Cloud-Architect certification exam is ideal for IT professionals who have experience in cloud computing and are seeking to enhance their skills and knowledge in cloud architecture. Google Certified Professional – Cloud Architect (GCP) certification is also suitable for individuals who want to demonstrate their expertise to potential employers and clients, as well as IT organizations that want to ensure that their employees have the necessary skills to design and manage cloud solutions using GCP.

NO.160 You have found an error in your App Engine application caused by missing Cloud Datastore indexes. You have created a YAML file with the required indexes and want to deploy these new indexes to Cloud Datastore.
What should you do?

Point gcloud datastore create-indexes to your configuration file

Upload the configuration file the App Engine’s default Cloud Storage bucket, and have App Engine detect the new indexes

In the GCP Console, use Datastore Admin to delete the current indexes and upload the new configuration file

Create an HTTP request to the built-in python module to send the index configuration file to your application

NO.161 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.162 For this question refer to the TerramEarth case study
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 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.

NO.163 Your company just finished a rapid lift and shift to Google Compute Engine for your compute needs. You have another 9 months to design and deploy a more cloud-native solution. Specifically, you want a system that is no-ops and auto-scaling. Which two compute products should you choose? Choose 2 answers

Compute Engine with containers

Google Container Engine with containers

Google App Engine Standard Environment

Compute Engine with custom instance types

Compute Engine with managed instance groups

NO.164 You need to upload files from your on-premises environment to Cloud Storage. You want the files to be encrypted on Cloud Storage using customer-supplied encryption keys. What should you do?

Supply the encryption key in a .boto configuration file. Use gsutil to upload the files.

Supply the encryption key using gcloud config. Use gsutil to upload the files to that bucket.

Use gsutil to upload the files, and use the flag –encryption-key to supply the encryption key.

Use gsutil to create a bucket, and use the flag –encryption-key to supply the encryption key. Use gsutil to upload the files to that bucket.

NO.165 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.

NO.166 A development manager is building a new application He asks you to review his requirements and identify what cloud technologies he can use to meet them. The application must
1. Be based on open-source technology for cloud portability
2. Dynamically scale compute capacity based on demand
3. Support continuous software delivery
4. Run multiple segregated copies of the same application stack
5. Deploy application bundles using dynamic templates
6. Route network traffic to specific services based on URL
Which combination of technologies will meet all of his requirements?

Google Container Engine, Jenkins, and Helm

Google Container Engine and Cloud Load Balancing

Google Compute Engine and Cloud Deployment Manager

Google Compute Engine, Jenkins, and Cloud Load Balancing

NO.167 You want to optimize the performance of an accurate, real-time, weather-charting application. The data comes from 50,000 sensors sending 10 readings a second, in the format of a timestamp and sensor reading. Where should you store the data?

Google BigQuery

Google Cloud SQL

Google Cloud Bigtable

Google Cloud Storage

NO.168 You want your Google Kubernetes Engine cluster to automatically add or remove nodes based on CPUload. What should you do?

Configure a HorizontalPodAutoscaler with a target CPU usage. Enable the Cluster Autoscaler from the GCP Console.

Configure a HorizontalPodAutoscaler with a target CPU usage. Enable autoscaling on the managed instance group for the cluster using the gcloud command.

Create a deployment and set the maxUnavailable and maxSurge properties. Enable the Cluster Autoscaler using the gcloud command.

Create a deployment and set the maxUnavailable and maxSurge properties. Enable autoscaling on the cluster managed instance group from the GCP Console.

NO.169 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 two.)

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, backend by Google Cloud SQL.

NO.170 Your customer runs a web service used by e-commerce sites to offer product recommendations to users.
the company has begun experimenting with a machine learning model on Google Cloud Platform to
improve the quality of results.
What should the customer do to improve their model’s results over time?

Export Cloud Machine Learning Engine performance metrics from Stackdriver to BigQuery, to be used
to analyze the efficiency of the model.

Build a roadmap to move the machine learning model training from Cloud GPUs to Cloud TPUs, which
offer better results.

Monitor Compute Engine announcements for availability of newer CPU architectures, and deploy the
model to them as soon as they are available for additional performance.

Save a history of recommendations and results of the recommendations in BigQuery, to be used as
training data.

NO.171 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.
Your agricultural division is experimenting with fully autonomous vehicles. You want your architecture to promote strong security during vehicle operation.
Which two architectures should you consider? (Choose two.)

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.

NO.172 For this question, refer to the EHR Healthcare case study. You are a developer on the EHR customer portal team. Your team recently migrated the customer portal application to Google Cloud. The load has increased on the application servers, and now the application is logging many timeout errors. You recently incorporated Pub/Sub into the application architecture, and the application is not logging any Pub/Sub publishing errors. You want to improve publishing latency. What should you do?

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.

NO.173 For this question, refer to the TerramEarth case study.
TerramEarth has equipped unconnected trucks with servers and sensors to collet telemetry data. Next year they want to use the data to train machine learning models. They want to store this data in the cloud while reducing costs. What should they do?

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.

NO.174 Your BigQuery project has several users. For audit purposes, you need to see how many queries each user ran in the last month.

Connect Google Data Studio to BigQuery. Create a dimension for the users and a metric for the amount of queries per user.

In the BigQuery interface, execute a query on the JOBS table to get the required information.

Use `bq show’ to list all jobs. Per job, use `bq Is’ to list job information and get the required information.

Use Cloud Audit Logging to view Cloud Audit Logs, and create a filter on the query operation to get the required information.

NO.175 You are designing a mobile chat application. You want to ensure that people cannot spoof chat messages by proving that a message was sent by a specific user. What should you do?

Encrypt the message client-side using block-based encryption with a shared key.

Tag messages client-side with the originating user identifier and the destination user.

Use a trusted certificate authority to enable SSL connectivity between the client application and the server.

Use public key infrastructure (PKI) to encrypt the message client-side using the originating user’s private key.

NO.176 For this question, refer to the Dress4Win case study.
At Dress4Win, an operations engineer wants to create a tow-cost solution to remotely archive copies of database backup files. The database files are compressed tar files stored in their current data center. How should he proceed?

Create a cron script using gsutil to copy the files to a Coldline Storage bucket.

Create a cron script using gsutil to copy the files to a Regional Storage bucket.

Create a Cloud Storage Transfer Service Job to copy the files to a Coldline Storage bucket.

Create a Cloud Storage Transfer Service job to copy the files to a Regional Storage bucket.

NO.177 For this question, refer to the Dress4Win case study. You are responsible for the security of data stored in Cloud Storage for your company, Dress4Win. You have already created a set of Google Groups and assigned the appropriate users to those groups. You should use Google best practices and implement the simplest design to meet the requirements.
Considering Dress4Win’s business and technical requirements, what should you do?

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.

NO.178 Case Study: 3 – JencoMart Case Study
Company Overview
JencoMart is a global retailer with over 10,000 stores in 16 countries. The stores carry a range of goods, such as groceries, tires, and jewelry. One of the company’s core values is excellent customer service. In addition, they recently introduced an environmental policy to reduce their carbon output by 50% over the next 5 years.
Company Background
JencoMart started as a general store in 1931, and has grown into one of the world’s leading brands known for great value and customer service. Over time, the company transitioned from only physical stores to a stores and online hybrid model, with 25% of sales online. Currently, JencoMart has little presence in Asia, but considers that market key for future growth.
Solution Concept
JencoMart wants to migrate several critical applications to the cloud but has not completed a technical review to determine their suitability for the cloud and the engineering required for migration. They currently host all of these applications on infrastructure that is at its end of life and is no longer supported.
Existing Technical Environment
JencoMart hosts all of its applications in 4 data centers: 3 in North American and 1 in Europe, most applications are dual-homed.
JencoMart understands the dependencies and resource usage metrics of their on-premises architecture.
Application Customer loyalty portal
LAMP (Linux, Apache, MySQL and PHP) application served from the two JencoMart-owned U.S.
data centers.
Database
* Oracle Database stores user profiles

* PostgreSQL database stores user credentials
-homed in US West

Authenticates all users
Compute
* 30 machines in US West Coast, each machine has:

* 20 machines in US East Coast, each machine has:
-core CPU

RAID 1)

Storage
* Access to shared 100 TB SAN in each location
* Tape backup every week
Business Requirements
* Optimize for capacity during peak periods and value during off-peak periods
* Guarantee service availably and support
* Reduce on-premises footprint and associated financial and environmental impact.
* Move to outsourcing model to avoid large upfront costs associated with infrastructure purchase
* Expand services into Asia.
Technical Requirements
* Assess key application for cloud suitability.
* Modify application for the cloud.
* Move applications to a new infrastructure.
* Leverage managed services wherever feasible
* Sunset 20% of capacity in existing data centers
* Decrease latency in Asia
CEO Statement
JencoMart will continue to develop personal relationships with our customers as more people access the web. The future of our retail business is in the global market and the connection between online and in-store experiences. As a large global company, we also have a responsibility to the environment through ‘green’ initiatives and polices.
CTO Statement
The challenges of operating data centers prevents focus on key technologies critical to our long- term success. Migrating our data services to a public cloud infrastructure will allow us to focus on big data and machine learning to improve our service customers.
CFO Statement
Since its founding JencoMart has invested heavily in our data services infrastructure. However, because of changing market trends, we need to outsource our infrastructure to ensure our long- term success. This model will allow us to respond to increasing customer demand during peak and reduce costs.
For this question, refer to the JencoMart case study.
The migration of JencoMart’s application to Google Cloud Platform (GCP) is progressing too slowly. The infrastructure is shown in the diagram.
You want to maximize throughput.
What are three potential bottlenecks? (Choose 3 answers.)

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

NO.179 Your company has decided to build a backup replica of their on-premises user authentication PostgreSQL database on Google Cloud Platform. The database is 4 TB, and large updates are frequent. Replication requires private address space communication. Which networking approach should you use?

Google Cloud Dedicated Interconnect

Google Cloud VPN connected to the data center network

A NAT and TLS translation gateway installed on-premises

A Google Compute Engine instance with a VPN server installed connected to the data center network

NO.180 You want to optimize the performance of an accurate, real-time, weather-charting application. The data comes from 50,000 sensors sending 10 readings a second, in the format of a timestamp and sensor reading.
Where should you store the data?

Google BigQuery

Google Cloud SQL

Google Cloud Bigtable

Google Cloud Storage

NO.181 You have created several preemptible Linux virtual machine instances using Google Compute Engine. You want to properly shut down your application before the virtual machines are preempted.
What should you do?

Create a shutdown script named k99.shutdown in the /etc/rc.6.d/ directory.

Create a shutdown script registered as a xinetd service in Linux and configure a Stackdnver endpoint check to call the service.

Create a shutdown script and use it as the value for a new metadata entry with the key shutdown-script in the Cloud Platform Console when you create the new virtual machine instance.

Create a shutdown script, registered as a xinetd service in Linux, and use the gcloud compute instances add-metadata command to specify the service URL as the value for a new metadata entry with the key shutdown-script-url

NO.182 Your company has an application deployed on Anthos clusters (formerly Anthos GKE) that is running multiple microservices. The cluster has both Anthos Service Mesh and Anthos Config Management configured. End users inform you that the application is responding very slowly. You want to identify the microservice that is causing the delay. What should you do?

Use the Service Mesh visualization in the Cloud Console to inspect the telemetry between the microservices.

Use Anthos Config Management to create a ClusterSelector selecting the relevant cluster. On the Google Cloud Console page for Google Kubernetes Engine, view the Workloads and filter on the cluster. Inspect the configurations of the filtered workloads.

Use Anthos Config Management to create a namespaceSelector selecting the relevant cluster namespace. On the Google Cloud Console page for Google Kubernetes Engine, visit the workloads and filter on the namespace. Inspect the configurations of the filtered workloads.

Reinstall istio using the default istio profile in order to collect request latency. Evaluate the telemetry between the microservices in the Cloud Console.