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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q293-Q298):

NEW QUESTION # 293
You have a Python web application with many dependencies that requires 0.1 CPU cores and 128 MB of memory to operate in production. You want to monitor and maximize machine utilization. You also want to reliably deploy new versions of the application. Which set of steps should you take?

A. Perform the following:
1. Create a managed instance group with n1-standard-1 type machines.
2. Build a Compute Engine image from the production branch that contains all of the dependencies and automatically starts the Python app.
3. Rebuild the Compute Engine image, and update the instance template to deploy new production releases.
B. Perform the following:
1. Create a GKE cluster with n1-standard-4 type machines.
2. Build a Docker image from the master branch with all of the dependencies, and tag it with 'latest'.
3. Create a Kubernetes Deployment in the default namespace with the imagePullPolicy set to 'Always'.
Restart the pods to automatically deploy new production releases.
C. Perform the following:
1. Create a Google Kubernetes Engine (GKE) cluster with n1-standard-1 type machines.
2. Build a Docker image from the production branch with all of the dependencies, and tag it with the version number.
3. Create a Kubernetes Deployment with the imagePullPolicy set to 'IfNotPresent' in the staging namespace, and then promote it to the production namespace after testing.
D. Perform the following:
1. Create a managed instance group with f1-micro type machines.
2. Use a startup script to clone the repository, check out the production branch, install the dependencies, and start the Python app.
3. Restart the instances to automatically deploy new production releases.

Answer: A

NEW QUESTION # 294
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?

A. 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.
B. 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.
C. 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.
D. 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.

Answer: C

NEW QUESTION # 295
For this question, refer to the Dress4Win case study. Considering the given business requirements, how would you automate the deployment of web and transactional data layers?

A. Deploy Nginx and Tomcat using Cloud Deployment Manager to Compute Engine. Deploy a Cloud SQL server to replace MySQL. Deploy Jenkins using Cloud Deployment Manager.
B. Migrate Nginx and Tomcat to App Engine. Deploy a MySQL server using Cloud Launcher. Deploy Jenkins to Compute Engine using Cloud Launcher.
C. Migrate Nginx and Tomcat to App Engine. Deploy a Cloud Datastore server to replace the MySQL server in a high-availability configuration. Deploy Jenkins to Compute Engine using Cloud Launcher.
D. Deploy Nginx and Tomcat using Cloud Launcher. Deploy a MySQL server using Cloud Launcher.
Deploy Jenkins to Compute Engine using Cloud Deployment Manager scripts.

Answer: C

Explanation:
Topic 6, TerramEarth Case 2
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.

NEW QUESTION # 296
Dress4Win has asked you to recommend machine types they should deploy their application servers to.
How should you proceed?

A. Perform a mapping of the on-premises physical hardware cores and RAM to the nearest machine types in the cloud.
B. Recommend that Dress4Win deploy application servers to machine types that offer the highest RAM to CPU ratio available.
C. Recommend that Dress4Win deploy into production with the smallest instances available, monitor them over time, and scale the machine type up until the desired performance is reached.
D. Identify the number of virtual cores and RAM associated with the application server virtual machines align them to a custom machine type in the cloud, monitor performance, and scale the machine types up until the desired performance is reached.

Answer: A

NEW QUESTION # 297
You are a cloud architect for a global financial services company. Your primary production environment is in a VPC network in Google Cloud. Your company also operates a secondary VPC network in Google Cloud and a separate, smaller analytics workload in another major cloud provider. You need to design a network architecture that provides reliable, high-bandwidth connectivity between all three environments. Your goal is to use Google ' s private backbone as much as possible for all intercloud and inter-VPC traffic to ensure low latency and simplify network management. What should you do?

A. Establish a Dedicated Interconnect connection with a 99.99% SLA to the other cloud provider. Use VPC Network Peering to connect the primary and secondary VPC networks. Configure BGP sessions to exchange routes.
B. Configure Cross-Cloud Interconnect to connect your primary VPC network to the other cloud provider ' s VPC. Configure Network Connectivity Center in your primary Google Cloud project. Create spokes for the Cross-Cloud Interconnect connection and the secondary VPC network.
C. Create dedicated HA VPN tunnels from your primary VPC network to both the secondary VPC network and the other cloud provider ' s VPC. Configure dynamic routing with Cloud Router to manage traffic between the environments.
D. Configure VPC Network Peering between the primary and secondary VPC networks. Establish a Cross- Cloud Interconnect connection from the primary VPC network to the other cloud provider ' s VPC.Configure routes to direct traffic from the secondary VPC network through the primary VPC to the other cloud.

Answer: B

Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From [Cloud Architect (GCP) Course Guide
/topics]: Cross-Cloud Interconnect provides high-bandwidth, private, and SLA-backed physical connectivity between Google Cloud and other hyperscale cloud service providers (such as AWS and Azure) without requiring intermediary colocation facilities. This leverages Google ' s global private fiber infrastructure directly to minimize latency and improve reliability for multicloud deployments.
Connecting multiple networks-specifically integrating a secondary Google Cloud VPC and an external cloud provider into a unified, transit-capable topology-requires Network Connectivity Center (NCC). Standard VPC Network Peering is strictly non-transitive; traffic from a peered secondary VPC cannot traverse the primary VPC to exit out of an Interconnect attachment. Network Connectivity Center addresses this constraint by acting as a centralized hub-and-spoke orchestration model over Google ' s backbone. By registering the Cross-Cloud Interconnect VLAN attachments and the secondary VPC network as spokes attached to an NCC hub, Google Cloud facilitates full, managed mesh routing and transit connectivity between the environments, centralizing management and keeping traffic on Google ' s private network.
References: Cloud Architect (GCP) Study Guide topics: " Cross-Cloud Interconnect Architecture " , " Network Connectivity Center (NCC) Hub and Spoke Topologies " , and " Transitive Routing Constraints in VPC Peering " .

NEW QUESTION # 298
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