You need to reduce the number of unplanned rollbacks of erroneous production deployments in your company's web hosting platform. Improvement to the QA/
Test processes accomplished an 80% reduction.
Which additional two approaches can you take to further reduce the rollbacks? (Choose two.)

A. Introduce a green-blue deployment model

B. Replace the QA environment with canary releases

C. Fragment the monolithic platform into microservices

D. Reduce the platform’s dependency on relational database systems

E. Replace the platform’s relational database systems with a NoSQL database

Your company wants to track whether someone is present in a meeting room reserved for a scheduled meeting. There are 1000 meeting rooms across 5 offices on 3 continents. Each room is equipped with a motion sensor that reports its status every second. The data from the motion detector includes only a sensor ID and several different discrete items of information. Analysts will use this data, together with information about account owners and office locations.
Which database type should you use?

A. Flat file

B. NoSQL

C. Relational

D. Blobstore

Your team needs to create a Google Kubernetes Engine (GKE) cluster to host a newly built application that requires access to third-party services on the internet.
Your company does not allow any Compute Engine instance to have a public IP address on Google Cloud. You need to create a deployment strategy that adheres to these guidelines. What should you do?

A. Configure the GKE cluster as a private cluster, and configure Cloud NAT Gateway for the cluster subnet.

B. Configure the GKE cluster as a private cluster. Configure Private Google Access on the Virtual Private Cloud (VPC).

C. Configure the GKE cluster as a route-based cluster. Configure Private Google Access on the Virtual Private Cloud (VPC).

D. Create a Compute Engine instance, and install a NAT Proxy on the instance. Configure all workloads on GKE to pass through this proxy to access third-party services on the Internet.

Your company has sensitive data in Cloud Storage buckets. Data analysts have Identity Access Management (IAM) permissions to read the buckets. You want to prevent data analysts from retrieving the data in the buckets from outside the office network. What should you do?

A. 1. Create a VPC Service Controls perimeter that includes the projects with the buckets. 2. Create an access level with the CIDR of the office network.

B. 1. Create a firewall rule for all instances in the Virtual Private Cloud (VPC) network for source range. 2. Use the Classless Inter-domain Routing (CIDR) of the office network.

C. 1. Create a Cloud Function to remove IAM permissions from the buckets, and another Cloud Function to add IAM permissions to the buckets. 2. Schedule the Cloud Functions with Cloud Scheduler to add permissions at the start of business and remove permissions at the end of business.

D. 1. Create a Cloud VPN to the office network. 2. Configure Private Google Access for on-premises hosts.

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?

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

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

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

D. 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.

The operations manager asks you for a list of recommended practices that she should consider when migrating a J2EE application to the cloud.
Which three practices should you recommend? (Choose three.)

A. Port the application code to run on Google App Engine

B. Integrate Cloud Dataflow into the application to capture real-time metrics

C. Instrument the application with a monitoring tool like Stackdriver Debugger

D. Select an automation framework to reliably provision the cloud infrastructure

E. Deploy a continuous integration tool with automated testing in a staging environment

F. Migrate from MySQL to a managed NoSQL database like Google Cloud Datastore or Bigtable

Your company creates rendering software which users can download from the company website. Your company has customers all over the world. You want to minimize latency for all your customers. You want to follow Google-recommended practices.
How should you store the files?

A. Save the files in a Multi-Regional Cloud Storage bucket.

B. Save the files in a Regional Cloud Storage bucket, one bucket per zone of the region.

C. Save the files in multiple Regional Cloud Storage buckets, one bucket per zone per region.

D. Save the files in multiple Multi-Regional Cloud Storage buckets, one bucket per multi-region.

Your company has a support ticketing solution that uses App Engine Standard. The project that contains the App Engine application already has a Virtual Private
Cloud (VPC) network fully connected to the company's on-premises environment through a Cloud VPN tunnel. You want to enable the App Engine application to communicate with a database that is running in the company's on-premises environment. What should you do?

A. Configure private Google access for on-premises hosts only.

B. Configure private Google access.

C. Configure private services access.

D. Configure serverless VPC access.

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.
You need to implement a network ingress for a new game that meets the defined business and technical requirements. Mountkirk Games wants each regional game instance to be located in multiple Google Cloud regions. What should you do?

A. Configure a global load balancer connected to a managed instance group running Compute Engine instances.

B. Configure kubemci with a global load balancer and Google Kubernetes Engine.

C. Configure a global load balancer with Google Kubernetes Engine.

D. Configure Ingress for Anthos with a global load balancer and Google Kubernetes Engine.

Company Overview -
Dress4Win is a web-based company that helps their users organize and manage their personal wardrobe using a web app and mobile application. The company also cultivates an active social network that connects their users with designers and retailers. They monetize their services through advertising, e-commerce, referrals, and a freemium app model. The application has grown from a few servers in the founder's garage to several hundred servers and appliances in a colocated data center. However, the capacity of their infrastructure is now insufficient for the application's rapid growth. Because of this growth and the company's desire to innovate faster, Dress4Win is committing to a full migration to a public cloud.
Solution Concept -
For the first phase of their migration to the cloud, Dress4Win is moving their development and test environments. They are also building a disaster recovery site, because their current infrastructure is at a single location. They are not sure which components of their architecture they can migrate as is and which components they need to change before migrating them.
Existing Technical Environment -
The Dress4Win application is served out of a single data center location. All servers run Ubuntu LTS v16.04.
Databases:
MySQL. 1 server for user data, inventory, static data:
- MySQL 5.8
- 8 core CPUs
- 128 GB of RAM
- 2x 5 TB HDD (RAID 1)
Redis 3 server cluster for metadata, social graph, caching. Each server is:
- Redis 3.2
- 4 core CPUs
- 32GB of RAM
Compute:
40 Web Application servers providing micro-services based APIs and static content.
`"
- Tomcat
Java -
- Nginx
- 4 core CPUs
- 32 GB of RAM
20 Apache Hadoop/Spark servers:
- Data analysis
- Real-time trending calculations
- 8 core CPUs
- 128 GB of RAM
- 4x 5 TB HDD (RAID 1)
3 RabbitMQ servers for messaging, social notifications, and events:
- 8 core CPUs
- 32GB of RAM
Miscellaneous servers:
- Jenkins, monitoring, bastion hosts, security scanners
- 8 core CPUs
- 32GB of RAM
Storage appliances:
iSCSI for VM hosts
Fiber channel SAN `" MySQL databases
- 1 PB total storage; 400 TB available
NAS `" image storage, logs, backups
- 100 TB total storage; 35 TB available
Business Requirements -
Build a reliable and reproducible environment with scaled parity of production.
Improve security by defining and adhering to a set of security and Identity and Access Management (IAM) best practices for cloud.
Improve business agility and speed of innovation through rapid provisioning of new resources.
Analyze and optimize architecture for performance in the cloud.
Technical Requirements -
Easily create non-production environments in the cloud.
Implement an automation framework for provisioning resources in cloud.
Implement a continuous deployment process for deploying applications to the on-premises datacenter or cloud.
Support failover of the production environment to cloud during an emergency.
Encrypt data on the wire and at rest.
Support multiple private connections between the production data center and cloud environment.
Executive Statement -
Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a competitor could use a public cloud platform to offset their up-front investment and free them to focus on developing better features. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80% of our capacity is sitting idle.
Our capital expenditure is now exceeding our quarterly projections. Migrating to the cloud will likely cause an initial increase in spending, but we expect to fully transition before our next hardware refresh cycle. Our total cost of ownership (TCO) analysis over the next 5 years for a public cloud strategy achieves a cost reduction between 30% and 50% over our current model.
For this question, refer to the Dress4Win case study. You want to ensure that your on-premises architecture meets business requirements before you migrate your solution.
What change in the on-premises architecture should you make?

A. Replace RabbitMQ with Google Pub/Sub.

B. Downgrade MySQL to v5.7, which is supported by Cloud SQL for MySQL.

C. Resize compute resources to match predefined Compute Engine machine types.

D. Containerize the micro-services and host them in Google Kubernetes Engine.

Company Overview -
Mountkirk Games makes online, session-based, multiplayer games for mobile platforms. They build all of their games using some server-side integration.
Historically, they have used cloud providers to lease physical servers.
Due to the unexpected popularity of some of their games, they have had problems scaling their global audience, application servers, MySQL databases, and analytics tools.
Their current model is to write game statistics to files and send them through an ETL tool that loads them into a centralized MySQL database for reporting.
Solution Concept -
Mountkirk Games is building a new game, which they expect to be very popular. They plan to deploy the game's backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics, and take advantage of its autoscaling server environment and integrate with a managed NoSQL database.
Business Requirements -
Increase to a global footprint
Improve uptime `" downtime is loss of players
Increase efficiency of the cloud resources we use
Reduce latency to all customers
Technical Requirements -
Requirements for Game Backend Platform
Dynamically scale up or down based on game activity
Connect to a transactional database service to manage user profiles and game state
Store game activity in a timeseries database service for future analysis
As the system scales, ensure that data is not lost due to processing backlogs
Run hardened Linux distro
Requirements for Game Analytics Platform
Dynamically scale up or down based on game activity
Process incoming data on the fly directly from the game servers
Process data that arrives late because of slow mobile networks
Allow queries to access at least 10 TB of historical data
Process files that are regularly uploaded by users' mobile devices
Executive Statement -
Our last successful game did not scale well with our previous cloud provider, resulting in lower user adoption and affecting the game's reputation. Our investors want more key performance indicators (KPIs) to evaluate the speed and stability of the game, as well as other metrics that provide deeper insight into usage patterns so we can adapt the game to target users. Additionally, our current technology stack cannot provide the scale we need, so we want to replace MySQL and move to an environment that provides autoscaling, low latency load balancing, and frees us up from managing physical servers.
For this question, refer to the Mountkirk Games case study. Which managed storage option meets Mountkirk's technical requirement for storing game activity in a time series database service?

A. Cloud Bigtable

B. Cloud Spanner

C. BigQuery

D. Cloud Datastore

Company overview -
TerramEarth manufactures heavy equipment for the mining and agricultural industries. 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 2 million TerramEarth vehicles in operation currently, and we see 20% yearly growth. Vehicles collect telemetry data from many sensors during operation. A small subset of critical data is transmitted from the vehicles in real time to facilitate fleet management. The rest of the sensor data is collected, compressed, and uploaded daily when the vehicles return to home base. Each vehicle usually generates 200 to 500 megabytes of data per day.
Existing technical environment -
TerramEarth's vehicle data aggregation and analysis infrastructure resides in Google Cloud and serves clients from all around the world. A growing amount of sensor data is captured from their two main manufacturing plants and sent to private data centers that contain their legacy inventory and logistics management systems. The private data centers have multiple network interconnects configured to Google Cloud. The web frontend for dealers and customers is running in
Google Cloud and allows access to stock management and analytics.
Business requirements -
* Predict and detect vehicle malfunction and rapidly ship parts to dealerships for just-in-time repair where possible.
* Decrease cloud operational costs and adapt to seasonality.
* Increase speed and reliability of development workflow.
* Allow remote developers to be productive without compromising code or data security.
* Create a flexible and scalable platform for developers to create custom API services for dealers and partners.
Technical requirements -
* Create a new abstraction layer for HTTP API access to their legacy systems to enable a gradual move into the cloud without disrupting operations.
* Modernize all CI/CD pipelines to allow developers to deploy container-based workloads in highly scalable environments.
* Allow developers to run experiments without compromising security and governance requirements.
* Create a self-service portal for internal and partner developers to create new projects, request resources for data analytics jobs, and centrally manage access to the API endpoints.
* Use cloud-native solutions for keys and secrets management and optimize for identity-based access.
* Improve and standardize tools necessary for application and network monitoring and troubleshooting.
Executive statement -
Our competitive advantage has always been our focus on the customer, with our ability to provide excellent customer service and minimize vehicle downtimes.
After moving multiple systems into Google Cloud, we are seeking new ways to provide best-in-class online fleet management services to our customers and improve operations of our dealerships. Our 5-year strategic plan is to create a partner ecosystem of new products by enabling access to our data, increasing autonomous operation capabilities of our vehicles, and creating a path to move the remaining legacy systems to the cloud.
For this question, refer to the TerramEarth case study. You are building a microservice-based application for TerramEarth. The application is based on Docker containers. You want to follow Google-recommended practices to build the application continuously and store the build artifacts. What should you do?

A. Configure a trigger in Cloud Build for new source changes. Invoke Cloud Build to build container images for each microservice, and tag them using the code commit hash. Push the images to the Container Registry.

B. Configure a trigger in Cloud Build for new source changes. The trigger invokes build jobs and build container images for the microservices. Tag the images with a version number, and push them to Cloud Storage.

C. Create a Scheduler job to check the repo every minute. For any new change, invoke Cloud Build to build container images for the microservices. Tag the images using the current timestamp, and push them to the Container Registry.

D. Configure a trigger in Cloud Build for new source changes. Invoke Cloud Build to build one container image, and tag the image with the label ‘latest.’ Push the image to the Container Registry.

You are tasked with building an online analytical processing (OLAP) marketing analytics and reporting tool. This requires a relational database that can operate on hundreds of terabytes of data. What is the Google-recommended tool for such applications?

A. Cloud Spanner, because it is globally distributed

B. Cloud SQL, because it is a fully managed relational database

C. Cloud Firestore, because it offers real-time synchronization across devices

D. BigQuery, because it is designed for large-scale processing of tabular data

Company Overview -
Dress4Win is a web-based company that helps their users organize and manage their personal wardrobe using a website and mobile application. The company also cultivates an active social network that connects their users with designers and retailers. They monetize their services through advertising, e-commerce, referrals, and a premium app model.
Company Background -
Dress4Win's application has grown from a few servers in the founder's garage to several hundred servers and appliances in a collocated data center. However, the capacity of their infrastructure is now insufficient for the application's rapid growth. Because of this growth and the company's desire to innovate faster,
Dress4Win is committing to a full migration to a public cloud.
Solution Concept -
For the first phase of their migration to the cloud, Dress4Win is considering moving their development and test environments. They are also considering building a disaster recovery site, because their current infrastructure is at a single location. They are not sure which components of their architecture they can migrate as is and which components they need to change before migrating them.
Existing Technical Environment -
The Dress4Win application is served out of a single data center location.
Databases:
- MySQL - user data, inventory, static data
- Redis - metadata, social graph, caching
Application servers:
- Tomcat - Java micro-services
- Nginx - static content
- Apache Beam - Batch processing
Storage appliances:
- iSCSI for VM hosts
- Fiber channel SAN - MySQL databases
- NAS - image storage, logs, backups
Apache Hadoop/Spark servers:
- Data analysis
- Real-time trending calculations
MQ servers:
- Messaging
- Social notifications
- Events
Miscellaneous servers:
- Jenkins, monitoring, bastion hosts, security scanners
Business Requirements -
Build a reliable and reproducible environment with scaled parity of production.
 
Improve security by defining and adhering to a set of security and Identity and Access Management (IAM) best practices for cloud.
Improve business agility and speed of innovation through rapid provisioning of new resources.
Analyze and optimize architecture for performance in the cloud.
Migrate fully to the cloud if all other requirements are met.
Technical Requirements -
Evaluate and choose an automation framework for provisioning resources in cloud.
Support failover of the production environment to cloud during an emergency.
Identify production services that can migrate to cloud to save capacity.
Use managed services whenever possible.
Encrypt data on the wire and at rest.
Support multiple VPN connections between the production data center and cloud environment.
CEO Statement -
Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a new competitor could use a public cloud platform to offset their up-front investment and freeing them to focus on developing better features.
CTO Statement -
We have invested heavily in the current infrastructure, but much of the equipment is approaching the end of its useful life. We are consistently waiting weeks for new gear to be racked before we can start new projects. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80% of our capacity is sitting idle.
CFO Statement -
Our capital expenditure is now exceeding our quarterly projections. Migrating to the cloud will likely cause an initial increase in spending, but we expect to fully transition before our next hardware refresh cycle. Our total cost of ownership (TCO) analysis over the next 5 years puts a cloud strategy between 30 to 50% lower than our current model.
Dress4Win has asked you for advice on how to migrate their on-premises MySQL deployment to the cloud.
They want to minimize downtime and performance impact to their on-premises solution during the migration.
Which approach should you recommend?

A. Create a dump of the on-premises MySQL master server, and then shut it down, upload it to the cloud environment, and load into a new MySQL cluster.

B. Setup a MySQL replica server/slave in the cloud environment, and configure it for asynchronous replication from the MySQL master server on-premises until cutover.

C. Create a new MySQL cluster in the cloud, configure applications to begin writing to both on premises and cloud MySQL masters, and destroy the original cluster at cutover.

D. Create a dump of the MySQL replica server into the cloud environment, load it into: Google Cloud Datastore, and configure applications to read/write to Cloud Datastore at cutover.

Company overview -
TerramEarth manufactures heavy equipment for the mining and agricultural industries. 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 2 million TerramEarth vehicles in operation currently, and we see 20% yearly growth. Vehicles collect telemetry data from many sensors during operation. A small subset of critical data is transmitted from the vehicles in real time to facilitate fleet management. The rest of the sensor data is collected, compressed, and uploaded daily when the vehicles return to home base. Each vehicle usually generates 200 to 500 megabytes of data per day.
Existing technical environment -
TerramEarth's vehicle data aggregation and analysis infrastructure resides in Google Cloud and serves clients from all around the world. A growing amount of sensor data is captured from their two main manufacturing plants and sent to private data centers that contain their legacy inventory and logistics management systems. The private data centers have multiple network interconnects configured to Google Cloud. The web frontend for dealers and customers is running in
Google Cloud and allows access to stock management and analytics.
Business requirements -
* Predict and detect vehicle malfunction and rapidly ship parts to dealerships for just-in-time repair where possible.
* Decrease cloud operational costs and adapt to seasonality.
* Increase speed and reliability of development workflow.
* Allow remote developers to be productive without compromising code or data security.
* Create a flexible and scalable platform for developers to create custom API services for dealers and partners.
Technical requirements -
* Create a new abstraction layer for HTTP API access to their legacy systems to enable a gradual move into the cloud without disrupting operations.
* Modernize all CI/CD pipelines to allow developers to deploy container-based workloads in highly scalable environments.
* Allow developers to run experiments without compromising security and governance requirements.
* Create a self-service portal for internal and partner developers to create new projects, request resources for data analytics jobs, and centrally manage access to the API endpoints.
* Use cloud-native solutions for keys and secrets management and optimize for identity-based access.
* Improve and standardize tools necessary for application and network monitoring and troubleshooting.
Executive statement -
Our competitive advantage has always been our focus on the customer, with our ability to provide excellent customer service and minimize vehicle downtimes.
After moving multiple systems into Google Cloud, we are seeking new ways to provide best-in-class online fleet management services to our customers and improve operations of our dealerships. Our 5-year strategic plan is to create a partner ecosystem of new products by enabling access to our data, increasing autonomous operation capabilities of our vehicles, and creating a path to move the remaining legacy systems to the cloud.
For this question, refer to the TerramEarth case study. You are migrating a Linux-based application from your private data center to Google Cloud. The
TerramEarth security team sent you several recent Linux vulnerabilities published by Common Vulnerabilities and Exposures (CVE). You need assistance in understanding how these vulnerabilities could impact your migration. What should you do? (Choose two.)

A. Open a support case regarding the CVE and chat with the support engineer.

B. Read the CVEs from the Google Cloud Status Dashboard to understand the impact.

C. Read the CVEs from the Google Cloud Platform Security Bulletins to understand the impact.

D. Post a question regarding the CVE in Stack Overflow to get an explanation.

E. Post a question regarding the CVE in a Google Cloud discussion group to get an explanation.

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?

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

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

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

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

You are helping the QA team to roll out a new load-testing tool to test the scalability of your primary cloud services that run on Google Compute Engine with Cloud
Bigtable.
Which three requirements should they include? (Choose three.)

A. Ensure that the load tests validate the performance of Cloud Bigtable

B. Create a separate Google Cloud project to use for the load-testing environment

C. Schedule the load-testing tool to regularly run against the production environment

D. Ensure all third-party systems your services use is capable of handling high load

E. Instrument the production services to record every transaction for replay by the load-testing tool

F. Instrument the load-testing tool and the target services with detailed logging and metrics collection

You are working at a financial institution that stores mortgage loan approval documents on Cloud Storage. Any change to these approval documents must be uploaded as a separate approval file. You need to ensure that these documents cannot be deleted or overwritten for the next 5 years. What should you do?

A. Create a retention policy on the bucket for the duration of 5 years. Create a lock on the retention policy.

B. Create a retention policy organizational constraint constraints/storage.retentionPolicySeconds at the organization level. Set the duration to 5 years.

C. Use a customer-managed key for the encryption of the bucket. Rotate the key after 5 years.

D. Create a retention policy organizational constraint constraints/storage.retentionPolicySeconds at the project level. Set the duration to 5 years.

Your company is using BigQuery as its enterprise data warehouse. Data is distributed over several Google Cloud projects. All queries on BigQuery need to be billed on a single project. You want to make sure that no query costs are incurred on the projects that contain the data. Users should be able to query the datasets, but not edit them.
How should you configure users' access roles?

A. Add all users to a group. Grant the group the role of BigQuery user on the billing project and BigQuery dataViewer on the projects that contain the data.

B. Add all users to a group. Grant the group the roles of BigQuery dataViewer on the billing project and BigQuery user on the projects that contain the data.

C. Add all users to a group. Grant the group the roles of BigQuery jobUser on the billing project and BigQuery dataViewer on the projects that contain the data.

D. Add all users to a group. Grant the group the roles of BigQuery dataViewer on the billing project and BigQuery jobUser on the projects that contain the data.

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

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

B. 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.

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

D. 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.

You write a Python script to connect to Google BigQuery from a Google Compute Engine virtual machine. The script is printing errors that it cannot connect to
BigQuery.
What should you do to fix the script?

A. Install the latest BigQuery API client library for Python

B. Run your script on a new virtual machine with the BigQuery access scope enabled

C. Create a new service account with BigQuery access and execute your script with that user

D. Install the bq component for gcloud with the command gcloud components install bq.

You want to establish a Compute Engine application in a single VPC across two regions. The application must communicate over VPN to an on-premises network.
How should you deploy the VPN?

A. Use VPC Network Peering between the VPC and the on-premises network.

B. Expose the VPC to the on-premises network using IAM and VPC Sharing.

C. Create a global Cloud VPN Gateway with VPN tunnels from each region to the on-premises peer gateway.

D. Deploy Cloud VPN Gateway in each region. Ensure that each region has at least one VPN tunnel to the on-premises peer gateway.

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 our 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?

A. Google Kubernetes Engine with an SSL Ingress

B. Cloud IoT Core with public/private key pairs

C. Compute Engine with project-wide SSH keys

D. Compute Engine with specific SSH keys

Your company operates nationally and plans to use GCP for multiple batch workloads, including some that are not time-critical. You also need to use GCP services that are HIPAA-certified and manage service costs.
How should you design to meet Google best practices?

A. Provision preemptible VMs to reduce cost. Discontinue use of all GCP services and APIs that are not HIPAA-compliant.

B. Provision preemptible VMs to reduce cost. Disable and then discontinue use of all GCP services and APIs that are not HIPAA-compliant.

C. Provision standard VMs in the same region to reduce cost. Discontinue use of all GCP services and APIs that are not HIPAA-compliant.

D. Provision standard VMs to the same region to reduce cost. Disable and then discontinue use of all GCP services and APIs that are not HIPAA-compliant.

You are configuring the cloud network architecture for a newly created project in Google Cloud that will host applications in Compute Engine. Compute Engine virtual machine instances will be created in two different subnets (sub-a and sub-b) within a single region:
• Instances in sub-a will have public IP addresses.
• Instances in sub-b will have only private IP addresses.
To download updated packages, instances must connect to a public repository outside the boundaries of Google Cloud. You need to allow sub-b to access the external repository. What should you do?

A. Enable Private Google Access on sub-b.

B. Configure Cloud NAT and select sub-b in the NAT mapping section.

C. Configure a bastion host instance in sub-a to connect to instances in sub-b.

D. Enable Identity-Aware Proxy for TCP forwarding for instances in sub-b.

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 was 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, Terram Earth 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 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.
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 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. 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.
Which of TerramEarth's legacy enterprise processes will experience significant change as a result of increased Google Cloud Platform adoption?

A. Opex/capex allocation, LAN changes, capacity planning

B. Capacity planning, TCO calculations, opex/capex allocation

C. Capacity planning, utilization measurement, data center expansion

D. Data Center expansion, TCO calculations, utilization measurement

You are deploying an application to Google Cloud. The application is part of a system. The application in Google Cloud must communicate over a private network with applications in a non-Google Cloud environment. The expected average throughput is 200 kbps. The business requires:
• 99.99% system availability
• cost optimization
You need to design the connectivity between the locations to meet the business requirements. What should you provision?

A. An HA Cloud VPN gateway connected with two tunnels to an on-premises VPN gateway.

B. A Classic Cloud VPN gateway connected with two tunnels to an on-premises VPN gateway.

C. Two HA Cloud VPN gateways connected to two on-premises VPN gateways. Configure each HA Cloud VPN gateway to have two tunnels, each connected to different on-premises VPN gateways.

D. A Classic Cloud VPN gateway connected with one tunnel to an on-premises VPN gateway.

Company Overview -
Mountkirk Games makes online, session-based, multiplayer games for mobile platforms. They build all of their games using some server-side integration.
Historically, they have used cloud providers to lease physical servers.
Due to the unexpected popularity of some of their games, they have had problems scaling their global audience, application servers, MySQL databases, and analytics tools.
Their current model is to write game statistics to files and send them through an ETL tool that loads them into a centralized MySQL database for reporting.
Solution Concept -
Mountkirk Games is building a new game, which they expect to be very popular. They plan to deploy the game's backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics, and take advantage of its autoscaling server environment and integrate with a managed NoSQL database.
Business Requirements -
Increase to a global footprint
Improve uptime `" downtime is loss of players
Increase efficiency of the cloud resources we use
Reduce latency to all customers
Technical Requirements -
Requirements for Game Backend Platform
Dynamically scale up or down based on game activity
Connect to a transactional database service to manage user profiles and game state
Store game activity in a timeseries database service for future analysis
As the system scales, ensure that data is not lost due to processing backlogs
Run hardened Linux distro
Requirements for Game Analytics Platform
Dynamically scale up or down based on game activity
Process incoming data on the fly directly from the game servers
Process data that arrives late because of slow mobile networks
Allow queries to access at least 10 TB of historical data
Process files that are regularly uploaded by users' mobile devices
Executive Statement -
Our last successful game did not scale well with our previous cloud provider, resulting in lower user adoption and affecting the game's reputation. Our investors want more key performance indicators (KPIs) to evaluate the speed and stability of the game, as well as other metrics that provide deeper insight into usage patterns so we can adapt the game to target users. Additionally, our current technology stack cannot provide the scale we need, so we want to replace MySQL and move to an environment that provides autoscaling, low latency load balancing, and frees us up from managing physical servers.
For this question, refer to the Mountkirk Games case study. You are in charge of the new Game Backend Platform architecture. The game communicates with the backend over a REST API.
You want to follow Google-recommended practices. How should you design the backend?

A. Create an instance template for the backend. For every region, deploy it on a multi-zone managed instance group. Use an L4 load balancer.

B. Create an instance template for the backend. For every region, deploy it on a single-zone managed instance group. Use an L4 load balancer.

C. Create an instance template for the backend. For every region, deploy it on a multi-zone managed instance group. Use an L7 load balancer.

D. Create an instance template for the backend. For every region, deploy it on a single-zone managed instance group. Use an L7 load balancer.

You are developing a globally scaled frontend for a legacy streaming backend data API. This API expects events in strict chronological order with no repeat data for proper processing.
Which products should you deploy to ensure guaranteed-once FIFO (first-in, first-out) delivery of data?

A. Cloud Pub/Sub alone

B. Cloud Pub/Sub to Cloud Dataflow

C. Cloud Pub/Sub to Stackdriver

D. Cloud Pub/Sub to Cloud SQL

Company overview -
TerramEarth manufactures heavy equipment for the mining and agricultural industries. 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 2 million TerramEarth vehicles in operation currently, and we see 20% yearly growth. Vehicles collect telemetry data from many sensors during operation. A small subset of critical data is transmitted from the vehicles in real time to facilitate fleet management. The rest of the sensor data is collected, compressed, and uploaded daily when the vehicles return to home base. Each vehicle usually generates 200 to 500 megabytes of data per day.
Existing technical environment -
TerramEarth's vehicle data aggregation and analysis infrastructure resides in Google Cloud and serves clients from all around the world. A growing amount of sensor data is captured from their two main manufacturing plants and sent to private data centers that contain their legacy inventory and logistics management systems. The private data centers have multiple network interconnects configured to Google Cloud. The web frontend for dealers and customers is running in
Google Cloud and allows access to stock management and analytics.
Business requirements -
* Predict and detect vehicle malfunction and rapidly ship parts to dealerships for just-in-time repair where possible.
* Decrease cloud operational costs and adapt to seasonality.
* Increase speed and reliability of development workflow.
* Allow remote developers to be productive without compromising code or data security.
* Create a flexible and scalable platform for developers to create custom API services for dealers and partners.
Technical requirements -
* Create a new abstraction layer for HTTP API access to their legacy systems to enable a gradual move into the cloud without disrupting operations.
* Modernize all CI/CD pipelines to allow developers to deploy container-based workloads in highly scalable environments.
* Allow developers to run experiments without compromising security and governance requirements.
* Create a self-service portal for internal and partner developers to create new projects, request resources for data analytics jobs, and centrally manage access to the API endpoints.
* Use cloud-native solutions for keys and secrets management and optimize for identity-based access.
* Improve and standardize tools necessary for application and network monitoring and troubleshooting.
Executive statement -
Our competitive advantage has always been our focus on the customer, with our ability to provide excellent customer service and minimize vehicle downtimes.
After moving multiple systems into Google Cloud, we are seeking new ways to provide best-in-class online fleet management services to our customers and improve operations of our dealerships. Our 5-year strategic plan is to create a partner ecosystem of new products by enabling access to our data, increasing autonomous operation capabilities of our vehicles, and creating a path to move the remaining legacy systems to the cloud.
For this question, refer to the TerramEarth case study. You start to build a new application that uses a few Cloud Functions for the backend. One use case requires a Cloud Function func_display to invoke another Cloud Function func_query. You want func_query only to accept invocations from func_display. You also want to follow Google's recommended best practices. What should you do?

A. Create a token and pass it in as an environment variable to func_display. When invoking func_query, include the token in the request. Pass the same token to func_query and reject the invocation if the tokens are different.

B. Make func_query ‘Require authentication.’ Create a unique service account and associate it to func_display. Grant the service account invoker role for func_query. Create an id token in func_display and include the token to the request when invoking func_query.

C. Make func_query ‘Require authentication’ and only accept internal traffic. Create those two functions in the same VPC. Create an ingress firewall rule for func_query to only allow traffic from func_display.

D. Create those two functions in the same project and VPC. Make func_query only accept internal traffic. Create an ingress firewall for func_query to only allow traffic from func_display. Also, make sure both functions use the same service account.

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 our 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. Considering the technical requirements, how should you reduce the unplanned vehicle downtime in GCP?

A. Use BigQuery as the data warehouse. Connect all vehicles to the network and stream data into BigQuery using Cloud Pub/Sub and Cloud Dataflow. Use Google Data Studio for analysis and reporting.

B. Use BigQuery as the data warehouse. Connect all vehicles to the network and upload gzip files to a Multi-Regional Cloud Storage bucket using gcloud. Use Google Data Studio for analysis and reporting.

C. Use Cloud Dataproc Hive as the data warehouse. Upload gzip files to a Multi-Regional Cloud Storage bucket. Upload this data into BigQuery using gcloud. Use Google Data Studio for analysis and reporting.

D. Use Cloud Dataproc Hive as the data warehouse. Directly stream data into partitioned Hive tables. Use Pig scripts to analyze data.

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 our 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 need to implement a reliable, scalable GCP solution for the data warehouse for your company,
TerramEarth.
Considering the TerramEarth business and technical requirements, what should you do?

A. Replace the existing data warehouse with BigQuery. Use table partitioning.

B. Replace the existing data warehouse with a Compute Engine instance with 96 CPUs.

C. Replace the existing data warehouse with BigQuery. Use federated data sources.

D. Replace the existing data warehouse with a Compute Engine instance with 96 CPUs. Add an additional Compute Engine preemptible instance with 32 CPUs.

Your company recently acquired a company that has infrastructure in Google Cloud. Each company has its own Google Cloud organization. Each company is using a Shared Virtual Private Cloud (VPC) to provide network connectivity for its applications. Some of the subnets used by both companies overlap. In order for both businesses to integrate, the applications need to have private network connectivity. These applications are not on overlapping subnets. You want to provide connectivity with minimal re-engineering. What should you do?

A. Set up VPC peering and peer each Shared VPC together.

B. Migrate the projects from the acquired company into your company’s Google Cloud organization. Re-launch the instances in your companies Shared VPC.

C. Set up a Cloud VPN gateway in each Shared VPC and peer Cloud VPNs.

D. Configure SSH port forwarding on each application to provide connectivity between applications in the different Shared VPCs.

You are working at an institution that processes medical data. You are migrating several workloads onto Google Cloud. Company policies require all workloads to run on physically separated hardware, and workloads from different clients must also be separated. You created a sole-tenant node group and added a node for each client. You need to deploy the workloads on these dedicated hosts. What should you do?

A. Add the node group name as a network tag when creating Compute Engine instances in order to host each workload on the correct node group.

B. Add the node name as a network tag when creating Compute Engine instances in order to host each workload on the correct node.

C. Use node affinity labels based on the node group name when creating Compute Engine instances in order to host each workload on the correct node group.

D. Use node affinity labels based on the node name when creating Compute Engine instances in order to host each workload on the correct node.

Company Overview -
Dress4Win is a web-based company that helps their users organize and manage their personal wardrobe using a web app and mobile application. The company also cultivates an active social network that connects their users with designers and retailers. They monetize their services through advertising, e-commerce, referrals, and a freemium app model. The application has grown from a few servers in the founder's garage to several hundred servers and appliances in a colocated data center. However, the capacity of their infrastructure is now insufficient for the application's rapid growth. Because of this growth and the company's desire to innovate faster, Dress4Win is committing to a full migration to a public cloud.
Solution Concept -
For the first phase of their migration to the cloud, Dress4Win is moving their development and test environments. They are also building a disaster recovery site, because their current infrastructure is at a single location. They are not sure which components of their architecture they can migrate as is and which components they need to change before migrating them.
Existing Technical Environment -
The Dress4Win application is served out of a single data center location. All servers run Ubuntu LTS v16.04.
Databases:
MySQL. 1 server for user data, inventory, static data:
- MySQL 5.8
- 8 core CPUs
- 128 GB of RAM
- 2x 5 TB HDD (RAID 1)
Redis 3 server cluster for metadata, social graph, caching. Each server is:
- Redis 3.2
- 4 core CPUs
- 32GB of RAM
Compute:
40 Web Application servers providing micro-services based APIs and static content.
`"
- Tomcat
Java -
- Nginx
- 4 core CPUs
- 32 GB of RAM
20 Apache Hadoop/Spark servers:
- Data analysis
- Real-time trending calculations
- 8 core CPUs
- 128 GB of RAM
- 4x 5 TB HDD (RAID 1)
3 RabbitMQ servers for messaging, social notifications, and events:
- 8 core CPUs
- 32GB of RAM
Miscellaneous servers:
- Jenkins, monitoring, bastion hosts, security scanners
- 8 core CPUs
- 32GB of RAM
Storage appliances:
iSCSI for VM hosts
Fiber channel SAN `" MySQL databases
- 1 PB total storage; 400 TB available
NAS `" image storage, logs, backups
- 100 TB total storage; 35 TB available
Business Requirements -
Build a reliable and reproducible environment with scaled parity of production.
Improve security by defining and adhering to a set of security and Identity and Access Management (IAM) best practices for cloud.
Improve business agility and speed of innovation through rapid provisioning of new resources.
Analyze and optimize architecture for performance in the cloud.
Technical Requirements -
Easily create non-production environments in the cloud.
Implement an automation framework for provisioning resources in cloud.
Implement a continuous deployment process for deploying applications to the on-premises datacenter or cloud.
Support failover of the production environment to cloud during an emergency.
Encrypt data on the wire and at rest.
Support multiple private connections between the production data center and cloud environment.
Executive Statement -
Our investors are concerned about our ability to scale and contain costs with our current infrastructure. They are also concerned that a competitor could use a public cloud platform to offset their up-front investment and free them to focus on developing better features. Our traffic patterns are highest in the mornings and weekend evenings; during other times, 80% of our capacity is sitting idle.
Our capital expenditure is now exceeding our quarterly projections. Migrating to the cloud will likely cause an initial increase in spending, but we expect to fully transition before our next hardware refresh cycle. Our total cost of ownership (TCO) analysis over the next 5 years for a public cloud strategy achieves a cost reduction between 30% and 50% over our current model.
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 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.

B. 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.

C. 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.

D. 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.

Your company has an application that is running on multiple instances of Compute Engine. It generates 1 TB per day of logs. For compliance reasons, the logs need to be kept for at least two years. The logs need to be available for active query for 30 days. After that, they just need to be retained for audit purposes. You want to implement a storage solution that is compliant, minimizes costs, and follows Google-recommended practices. What should you do?

A. 1. Install a Cloud Logging agent on all instances. 2. Create a sink to export logs into a regional Cloud Storage bucket. 3. Create an Object Lifecycle rule to move files into a Coldline Cloud Storage bucket after one month. 4. Configure a retention policy at the bucket level using bucket lock.

B. 1. Write a daily cron job, running on all instances, that uploads logs into a Cloud Storage bucket. 2. Create a sink to export logs into a regional Cloud Storage bucket. 3. Create an Object Lifecycle rule to move files into a Coldline Cloud Storage bucket after one month.

C. 1. Install a Cloud Logging agent on all instances. 2. Create a sink to export logs into a partitioned BigQuery table. 3. Set a time_partitioning_expiration of 30 days.

D. 1. Create a daily cron job, running on all instances, that uploads logs into a partitioned BigQuery table. 2. Set a time_partitioning_expiration of 30 days.

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?

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

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

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

D. 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.

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?

A. Enable Virtual Private Cloud (VPC) flow logging.

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

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

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

Company overview -
EHR Healthcare is a leading provider of electronic health record software to the medical industry. EHR Healthcare provides their software as a service to multi- national medical offices, hospitals, and insurance providers.
Solution concept -
Due to rapid changes in the healthcare and insurance industry, EHR Healthcare's business has been growing exponentially year over year. They need to be able to scale their environment, adapt their disaster recovery plan, and roll out new continuous deployment capabilities to update their software at a fast pace. Google
Cloud has been chosen to replace their current colocation facilities.
Existing technical environment -
EHR's software is currently hosted in multiple colocation facilities. The lease on one of the data centers is about to expire.
Customer-facing applications are web-based, and many have recently been containerized to run on a group of Kubernetes clusters. Data is stored in a mixture of relational and NoSQL databases (MySQL, MS SQL Server, Redis, and MongoDB).
EHR is hosting several legacy file- and API-based integrations with insurance providers on-premises. These systems are scheduled to be replaced over the next several years. There is no plan to upgrade or move these systems at the current time.
Users are managed via Microsoft Active Directory. Monitoring is currently being done via various open source tools. Alerts are sent via email and are often ignored.
Business requirements -
* On-board new insurance providers as quickly as possible.
* Provide a minimum 99.9% availability for all customer-facing systems.
* Provide centralized visibility and proactive action on system performance and usage.
* Increase ability to provide insights into healthcare trends.
* Reduce latency to all customers.
* Maintain regulatory compliance.
* Decrease infrastructure administration costs.
* Make predictions and generate reports on industry trends based on provider data.
Technical requirements -
* Maintain legacy interfaces to insurance providers with connectivity to both on-premises systems and cloud providers.
* Provide a consistent way to manage customer-facing applications that are container-based.
* Provide a secure and high-performance connection between on-premises systems and Google Cloud.
* Provide consistent logging, log retention, monitoring, and alerting capabilities.
* Maintain and manage multiple container-based environments.
* Dynamically scale and provision new environments.
* Create interfaces to ingest and process data from new providers.
Executive statement -
Our on-premises strategy has worked for years but has required a major investment of time and money in training our team on distinctly different systems, managing similar but separate environments, and responding to outages. Many of these outages have been a result of misconfigured systems, inadequate capacity to manage spikes in traffic, and inconsistent monitoring practices. We want to use Google Cloud to leverage a scalable, resilient platform that can span multiple environments seamlessly and provide a consistent and stable user experience that positions us for future growth.
You need to upgrade the EHR connection to comply with their requirements. The new connection design must support business-critical needs and meet the same network and security policy requirements. What should you do?

A. Add a new Dedicated Interconnect connection.

B. Upgrade the bandwidth on the Dedicated Interconnect connection to 100 G.

C. Add three new Cloud VPN connections.

D. Add a new Carrier Peering connection.

Company overview -
Helicopter Racing League (HRL) is a global sports league for competitive helicopter racing. Each year HRL holds the world championship and several regional league competitions where teams compete to earn a spot in the world championship. HRL offers a paid service to stream the races all over the world with live telemetry and predictions throughout each race.
Solution concept -
HRL wants to migrate their existing service to a new platform to expand their use of managed AI and ML services to facilitate race predictions. Additionally, as new fans engage with the sport, particularly in emerging regions, they want to move the serving of their content, both real-time and recorded, closer to their users.
Existing technical environment -
HRL is a public cloud-first company; the core of their mission-critical applications runs on their current public cloud provider. Video recording and editing is performed at the race tracks, and the content is encoded and transcoded, where needed, in the cloud. Enterprise-grade connectivity and local compute is provided by truck-mounted mobile data centers. Their race prediction services are hosted exclusively on their existing public cloud provider. Their existing technical environment is as follows:
Existing content is stored in an object storage service on their existing public cloud provider.
Video encoding and transcoding is performed on VMs created for each job.
Race predictions are performed using TensorFlow running on VMs in the current public cloud provider.
Business requirements -
HRL's owners want to expand their predictive capabilities and reduce latency for their viewers in emerging markets. Their requirements are:
Support ability to expose the predictive models to partners.
Increase predictive capabilities during and before races:
ג—‹ Race results
ג—‹ Mechanical failures
ג—‹ Crowd sentiment
Increase telemetry and create additional insights.
Measure fan engagement with new predictions.
Enhance global availability and quality of the broadcasts.
Increase the number of concurrent viewers.
Minimize operational complexity.
Ensure compliance with regulations.
Create a merchandising revenue stream.
Technical requirements -
Maintain or increase prediction throughput and accuracy.
Reduce viewer latency.
Increase transcoding performance.
Create real-time analytics of viewer consumption patterns and engagement.
Create a data mart to enable processing of large volumes of race data.
Executive statement -
Our CEO, S. Hawke, wants to bring high-adrenaline racing to fans all around the world. We listen to our fans, and they want enhanced video streams that include predictions of events within the race (e.g., overtaking). Our current platform allows us to predict race outcomes but lacks the facility to support real-time predictions during races and the capacity to process season-long results.
For this question, refer to the Helicopter Racing League (HRL) case study. Your team is in charge of creating a payment card data vault for card numbers used to bill tens of thousands of viewers, merchandise consumers, and season ticket holders. You need to implement a custom card tokenization service that meets the following requirements:
*    It must provide low latency at minimal cost.
*    It must be able to identify duplicate credit cards and must not store plaintext card numbers.
*    It should support annual key rotation.
Which storage approach should you adopt for your tokenization service?

A. Store the card data in Secret Manager after running a query to identify duplicates.

B. Encrypt the card data with a deterministic algorithm stored in Firestore using Datastore mode.

C. Encrypt the card data with a deterministic algorithm and shard it across multiple Memorystore instances.

D. Use column-level encryption to store the data in Cloud SQL.

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?

A. Google Cloud Dedicated Interconnect

B. Google Cloud VPN connected to the data center network

C. A NAT and TLS translation gateway installed on-premises

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

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 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.

B. 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.

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 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.

You need to deploy a stateful workload on Google Cloud. The workload can scale horizontally, but each instance needs to read and write to the same POSIX filesystem. At high load, the stateful workload needs to support up to 100 MB/s of writes. What should you do?

A. Use a persistent disk for each instance.

B. Use a regional persistent disk for each instance.

C. Create a Cloud Filestore instance and mount it in each instance.

D. Create a Cloud Storage bucket and mount it in each instance using gcsfuse.

Your company is planning to upload several important files to Cloud Storage. After the upload is completed, they want to verify that the uploaded content is identical to what they have on-premises. You want to minimize the cost and effort of performing this check. What should you do?

A. 1. Use Linux shasum to compute a digest of files you want to upload. 2. Use gsutil -m to upload all the files to Cloud Storage. 3. Use gsutil cp to download the uploaded files. 4. Use Linux shasum to compute a digest of the downloaded files. 5. Compare the hashes.

B. 1. Use gsutil -m to upload the files to Cloud Storage. 2. Develop a custom Java application that computes CRC32C hashes. 3. Use gsutil ls -L gs://[YOUR_BUCKET_NAME] to collect CRC32C hashes of the uploaded files. 4. Compare the hashes.

C. 1. Use gsutil -m to upload all the files to Cloud Storage. 2. Use gsutil cp to download the uploaded files. 3. Use Linux diff to compare the content of the files.

D. 1. Use gsutil -m to upload the files to Cloud Storage. 2. Use gsutil hash -c FILE_NAME to generate CRC32C hashes of all on-premises files. 3. Use gsutil ls -L gs://[YOUR_BUCKET_NAME] to collect CRC32C hashes of the uploaded files. 4. Compare the hashes.

Google Cloud Platform resources are managed hierarchically using organization, folders, and projects. When Cloud Identity and Access Management (IAM) policies exist at these different levels, what is the effective policy at a particular node of the hierarchy?

A. The effective policy is determined only by the policy set at the node

B. The effective policy is the policy set at the node and restricted by the policies of its ancestors

C. The effective policy is the union of the policy set at the node and policies inherited from its ancestors

D. The effective policy is the intersection of the policy set at the node and policies inherited from its ancestors

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
- 20 TB
- Complex table structure
- Well maintained, clean data
- Strong backup strategy
PostgreSQL database stores user credentials
- Single-homed in US West
- No redundancy
- Backed up every 12 hours
- 100% uptime service level agreement (SLA)
- Authenticates all users
Compute -
30 machines in US West Coast, each machine has:
- Twin, dual core CPUs
- 32 GB of RAM
- Twin 250 GB HDD (RAID 1)
20 machines in US East Coast, each machine has:
- Single, dual-core CPU
- 24 GB of RAM
- Twin 250 GB HDD (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 availability 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 applications 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 policies.
CTO Statement -
The challenges of operating data centers prevent 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 to 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 periods and reduce costs.
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 three.)

A. Delete the virtual machine (VM) and disks and create a new one

B. Delete the instance, attach the disk to a new VM, and investigate

C. Take a snapshot of the disk and connect to a new machine to investigate

D. Check inbound firewall rules for the network the machine is connected to

E. Connect the machine to another network with very simple firewall rules and investigate

F. Print the Serial Console output for the instance for troubleshooting, activate the interactive console, and investigate

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?

A. 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.

B. 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.

C. 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.

D. 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.

Your company has a Kubernetes application that pulls messages from Pub/Sub and stores them in Filestore. 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?

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

B. Configure a Kubernetes autoscaling deployment based on the subscription/push_request_latencies metric.

C. Use the –enable-autoscaling flag when you create the Kubernetes cluster.

D. Configure a Kubernetes autoscaling deployment based on the subscription/num_undelivered_messages metric.

The development team has provided you with a Kubernetes Deployment file. You have no infrastructure yet and need to deploy the application. What should you do?

A. Use gcloud to create a Kubernetes cluster. Use Deployment Manager to create the deployment.

B. Use gcloud to create a Kubernetes cluster. Use kubectl to create the deployment.

C. Use kubectl to create a Kubernetes cluster. Use Deployment Manager to create the deployment.

D. Use kubectl to create a Kubernetes cluster. Use kubectl to create the deployment.

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 was 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, Terram Earth 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 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.
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 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. 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.
TerramEarth's CTO wants to use the raw data from connected vehicles to help identify approximately when a vehicle in the field will have a catastrophic failure.
You want to allow analysts to centrally query the vehicle data.
Which architecture should you recommend?
A.
 
B.
 
C.
 
D.