Provide Google Professional-Cloud-Architect Practice Test Engine for Preparation [Q48-Q72]

Q48. Your company has a stateless web API that performs scientific calculations. The web API runs on a single Google Kubernetes Engine (GKE) cluster. The cluster is currently deployed in us-central1. Your company has expanded to offer your API to customers in Asi a. You want to reduce the latency for the users in Asia. What should you do?

Use a global HTTP(s) load balancer with Cloud CDN enabled

Create a second GKE cluster in asia-southeast1, and expose both API’s using a Service of type Load Balancer. Add the public Ips to the Cloud DNS zone

Increase the memory and CPU allocated to the application in the cluster

Create a second GKE cluster in asia-southeast1, and use kubemci to create a global HTTP(s) load balancer

Q49. 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.

Q50. 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

Q51. All compute Engine instances in your VPC should be able to connect to an Active Directory server on
specific ports. Any other traffic emerging from your instances is not allowed. You want to enforce this using
VPC firewall rules.
How should you configure the firewall rules?

Create an egress rule with priority 1000 to deny all traffic for all instances. Create another egress rule
with priority 100 to allow the Active Directory traffic for all instances.

Create an egress rule with priority 100 to deny all traffic for all instances. Create another egress rule
with priority 1000 to allow the Active Directory traffic for all instances.

Create an egress rule with priority 1000 to allow the Active Directory traffic. Rely on the implied deny
egress rule with priority 100 to block all traffic for all instances.

Create an egress rule with priority 100 to allow the Active Directory traffic. Rely on the implied deny
egress rule with priority 1000 to block all traffic for all instances.

Q52. For this question, refer to the Mountkirk Games case study.
Mountkirk Games wants to set up a continuous delivery pipeline. Their architecture includes many small services that they want to be able to update and roll back quickly. Mountkirk Games has the following requirements:
* Services are deployed redundantly across multiple regions in the US and Europe.
* Only frontend services are exposed on the public internet.
* They can provide a single frontend IP for their fleet of services.
* Deployment artifacts are immutable.
Which set of products should they use?

Google Cloud Storage, Google Cloud Dataflow, Google Compute Engine

Google Cloud Storage, Google App Engine, Google Network Load Balancer

Google Kubernetes Registry, Google Container Engine, Google HTTP(S) Load Balancer

Google Cloud Functions, Google Cloud Pub/Sub, Google Cloud Deployment Manager

Q53. For this question, refer to the Mountkirk Games case study. Mountkirk Games wants to design their solution for the future in order to take advantage of cloud and technology improvements as they become available.
Which two steps should they take? (Choose two.)

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.

Q54. 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

Q55. 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.

Q56. You set up an autoscaling instance group to serve web traffic for an upcoming launch. After configuring the instance group as a backend service to an HTTP(S) load balancer, you notice that virtual machine (VM) instances are being terminated and re-launched every minute. The instances do not have a public IP address. You have verified the appropriate web response is coming from each instance using the curl command. You want to ensure the backend is configured correctly. What should you do?

Ensure that a firewall rule exists to allow source traffic on HTTP/HTTPS to reach the load balancer.

Assign a public IP to each instance and configure a firewall rule to allow the load balancer to reach the instance public IP.

Ensure that a firewall rule exists to allow load balancer health checks to reach the instances in the instance group.

Create a tag on each instance with the name of the load balancer. Configure a firewall rule with the name of the load balancer as the source and the instance tag as the destination.

Q57. You have been asked to select the storage system for the click-data of your company’s large portfolio of websites. This data is streamed in from a custom website analytics package at a typical rate of 6,000 clicks per minute, with bursts of up to 8,500 clicks per second. It must been stored for future analysis by your data science and user experience teams. Which storage infrastructure should you choose?

Google Cloud SQL

Google Cloud Bigtable

Google Cloud Storage

Google cloud Datastore

Q58. You have an App Engine application that needs to be updated. You want to test the update with production traffic before replacing the current application version.
What should you do?

Deploy the update using the Instance Group Updater to create a partial rollout, which allows for canary testing.

Deploy the update as a new version in the App Engine application, and split traffic between the new and current versions.

Deploy the update in a new VPC, and use Google’s global HTTP load balancing to split traffic between the update and current applications.

Deploy the update as a new App Engine application, and use Google’s global HTTP load balancing to split traffic between the new and current applications.

Q59. Your customer is moving their corporate applications to Google Cloud Platform. The security team wants detailed visibility of all projects in the organization. You provision the Google Cloud Resource Manager and set up yourself as the org admin. What Google Cloud Identity and Access Management (Cloud IAM) roles should you give to the security team’?

Org viewer, project owner

Org viewer, project viewer

Org admin, project browser

Project owner, network admin

Q60. A lead engineer wrote a custom tool that deploys virtual machines in the legacy data center. He wants to migrate the custom tool to the new cloud environment. You want to advocate for the adoption of Google Cloud Deployment Manager.
What are two business risks of migrating to Cloud Deployment Manager? Choose 2 answers.

Cloud Deployment Manager uses Python

Cloud Deployment Manager APIs could be deprecated in the future

Cloud Deployment Manager is unfamiliar to the company’s engineers

Cloud Deployment Manager requires a Google APIs service account to run

Cloud Deployment Manager can be used to permanently delete cloud resources

Cloud Deployment Manager only supports automation of Google Cloud resources

Q61. You are working with a data warehousing team that performs data analysis. The team needs to process data from external partners, but the data contains personally identifiable information (PlI). You need to process and store the data without storing any of the Pll dat a. What should you do?

Create a Dataflow pipeline to retrieve the data from the external sources. As part of the pipeline use the Cloud Data Loss Prevention (Cloud DLP) API to remove any Pll data Store the result in BigQuery

Create a Dataflow pipeline to retrieve the data from the external sources. As part of the pipeline store all non-PII data in BigQuery and store all Pll data in a Cloud Storage bucket that has a retention policy set.

Ask the external partners to upload an data on Cloud Storage Configure Bucket Lock for the bucket Create a Dataflow pipeline to read the data from the bucket As part of the pipeline, use the Cloud Data Loss Prevention (Cloud DIP) API to remove any Pll data Store the result in BigQuery

Ask the external partners to import ail data in your BigQuery dataset Create a dataflow pipeline to copy the data into a new table As part of the Dataflow bucket skip all data in columns that have Pll data

Q62. For this question, refer to the Dress4Win case study.
Dress4Win would like to become familiar with deploying applications to the cloud by successfully deploying some applications quickly, as is. They have asked for your recommendation. What should you advise?

Identify self-contained applications with external dependencies as a first move to the cloud.

Identify enterprise applications with internal dependencies and recommend these as a first move to the cloud.

Suggest moving their in-house databases to the cloud and continue serving requests to on-premise applications.

Recommend moving their message queuing servers to the cloud and continue handling requests to on-premise applications.

Q63. You are building a continuous deployment pipeline for a project stored in a Git source repository and want to ensure that code changes can be verified deploying to production. What should you do?

Use Spinnaker to deploy builds to production using the red/black deployment strategy so that changes can easily be rolled back.

Use Spinnaker to deploy builds to production and run tests on production deployments.

Use Jenkins to build the staging branches and the master branch. Build and deploy changes to production for 10% of users before doing a complete rollout.

Use Jenkins to monitor tags in the repository. Deploy staging tags to a staging environment for testing.
After testing, tag the repository for production and deploy that to the production environment.

Q64. 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 dat a. 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 region bucket and use a Cloud Dataproc cluster to finish the jo

Q65. You have broken down a legacy monolithic application into a few containerized RESTful microservices. You want to run those microservices on Cloud Run. You also want to make sure the services are highly available with low latency to your customers. What should you do?

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

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

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

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

Q66. You are deploying an application on App Engine that needs to integrate with an on-premises database. For security purposes, your on-premises database must not be accessible through the public Internet.
What should you do?

Deploy your application on App Engine standard environment and use App Engine firewall rules to limit access to the open on-premises database.

Deploy your application on App Engine standard environment and use Cloud VPN to limit access to the on-premises database.

Deploy your application on App Engine flexible environment and use App Engine firewall rules to limit access to the on-premises database.

Deploy your application on App Engine flexible environment and use Cloud VPN to limit access to the on-premises database.

Q67. 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.
JencoMart has built a version of their application on Google Cloud Platform that serves traffic to Asia. You want to measure success against their business and technical goals. Which metrics should you track?

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

Q68. 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.

Q69. For this question, refer to the EHR Healthcare case study. You need to define the technical architecture for hybrid connectivity between EHR’s on-premises systems and Google Cloud. You want to follow Google’s recommended practices for production-level applications. Considering the EHR Healthcare business and technical requirements, what should you do?

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.

Q70. 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.

Q71. For this question, refer to the Helicopter Racing League (HRL) case study. HRL wants better prediction accuracy from their ML prediction models. They want you to use Google’s AI Platform so HRL can understand and interpret the predictions. What should you do?

Use Explainable AI.

Use Vision AI.

Use Google Cloud’s operations suite.

Use Jupyter Notebooks.

Q72. You are using Cloud CDN to deliver static HTTP(S) website content hosted on a Compute Engine instance group. You want to improve the cache hit ratio.
What should you do?

Customize the cache keys to omit the protocol from the key.

Shorten the expiration time of the cached objects.

Make sure the HTTP(S) header “Cache-Region” points to the closest region of your users.

Replicate the static content in a Cloud Storage bucket. Point CloudCDN toward a load balancer on that bucket.