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NEW QUESTION 53
For this question refer to the TerramEarth case study
Operational parameters such as oil pressure are adjustable on each of TerramEarth's vehicles to increase their efficiency, depending on their environmental conditions. Your primary goal is to increase the operating efficiency of all 20 million cellular and unconnected vehicles in the field How can you accomplish this goal?

A. Have your engineers inspect the data for patterns, and then create an algorithm with rules that make operational adjustments automatically.B. Capture all operating data, train machine learning models that identify ideal operations, and run locally to make operational adjustments automatically.C. Capture all operating data, train machine learning models that identify ideal operations, and host in Google Cloud Machine Learning (ML) Platform to make operational adjustments automatically.D. Implement a Google Cloud Dataflow streaming job with a sliding window, and use Google Cloud Messaging (GCM) to make operational adjustments automatically.

Answer: B

Explanation:
Topic 1, TerramEarth Case Study
Company Overview
TerramEarth manufactures heavy equipment for the mining and agricultural industries: About 80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in
100 countries. Their mission is to build products that make their customers more productive.
Company Background
TerramEarth formed in 1946, when several small, family owned companies combined to retool after World War II. The company cares about their employees and customers and considers them to be extended members of their family.
TerramEarth is proud of their ability to innovate on their core products and find new markets as their customers' needs change. For the past 20 years trends in the industry have been largely toward increasing productivity by using larger vehicles with a human operator.
Solution Concept
There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second. Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced. The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules.
Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second, with 22 hours of operation per day. TerramEarth collects a total of about 9 TB/day from these connected vehicles.
Existing Technical Environment

TerramEarth's existing architecture is composed of Linux-based systems that reside in a data center. These systems gzip CSV files from the field and upload via FTP, transform and aggregate them, and place the data in their data warehouse. Because this process takes time, aggregated reports are based on data that is 3 weeks old.
With this data, TerramEarth has been able to preemptively stock replacement parts and reduce unplanned downtime of their vehicles by 60%. However, because the data is stale, some customers are without their vehicles for up to 4 weeks while they wait for replacement parts.
Business Requirements
* Decrease unplanned vehicle downtime to less than 1 week, without increasing the cost of carrying surplus inventory
* Support the dealer network with more data on how their customers use their equipment IP better position new products and services.
* Have the ability to partner with different companies-especially with seed and fertilizer suppliers in the fast-growing agricultural business-to create compelling joint offerings for their customers CEO Statement We have been successful in capitalizing on the trend toward larger vehicles to increase the productivity of our customers. Technological change is occurring rapidly and TerramEarth has taken advantage of connected devices technology to provide our customers with better services, such as our intelligent farming equipment.
With this technology, we have been able to increase farmers' yields by 25%, by using past trends to adjust how our vehicles operate. These advances have led to the rapid growth of our agricultural product line, which we expect will generate 50% of our revenues by 2020.
CTO Statement
Our competitive advantage has always been in the manufacturing process with our ability to build better vehicles for tower cost than our competitors. However, new products with different approaches are constantly being developed, and I'm concerned that we lack the skills to undergo the next wave of transformations in our industry. Unfortunately, our CEO doesn't take technology obsolescence seriously and he considers the many new companies in our industry to be niche players. My goals are to build our skills while addressing immediate market needs through incremental innovations.

 

NEW QUESTION 54
Case Study: 3 - JencoMart Case Study
Company Overview
JencoMart is a global retailer with over 10,000 stores in 16 countries. The stores carry a range of goods, such as groceries, tires, and jewelry. One of the company's core values is excellent customer service. In addition, they recently introduced an environmental policy to reduce their carbon output by 50% over the next 5 years.
Company Background
JencoMart started as a general store in 1931, and has grown into one of the world's leading brands known for great value and customer service. Over time, the company transitioned from only physical stores to a stores and online hybrid model, with 25% of sales online. Currently, JencoMart has little presence in Asia, but considers that market key for future growth.
Solution Concept
JencoMart wants to migrate several critical applications to the cloud but has not completed a technical review to determine their suitability for the cloud and the engineering required for migration. They currently host all of these applications on infrastructure that is at its end of life and is no longer supported.
Existing Technical Environment
JencoMart hosts all of its applications in 4 data centers: 3 in North American and 1 in Europe, most applications are dual-homed.
JencoMart understands the dependencies and resource usage metrics of their on-premises architecture.
Application Customer loyalty portal
LAMP (Linux, Apache, MySQL and PHP) application served from the two JencoMart-owned U.S.
data centers.
Database
* Oracle Database stores user profiles




* PostgreSQL database stores user credentials
-homed in US West



service level agreement (SLA)


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



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



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

A. Provision service account keys for the on-premises infrastructure and use Google Cloud Platform (GCP) managed keys for the VMsB. Authenticate the on-premises infrastructure with a user account and provision service account keys for the VMs.C. Deploy a custom authentication service on GCE/Google Container Engine (GKE) for the on- premises infrastructure and use GCP managed keys for the VMs.D. Provision service account keys for the on-premises infrastructure and for the GCE virtual machines (VMs).

Answer: D

Explanation:
A) (correct answer) This addresses both of data migration and application server migration properly.
"Provision service account keys for the on-premises infrastructure": For code running on systems outside Google, you cannot use GCP-managed keys. You need to create Service account for it and provision User-managed keys. These keys are created, downloadable, and managed by users - This is solution for on-premises access to GCP datastore during migration
"use Google Cloud Platform (GCP) managed keys for the VMs" - this is solution for Application server migration since there is no external access to GCP is required during the migration.
Answer B is incorrect: First, the applications running on-premises to access GCP Datastore assume the identity of the service account to call Google APIs, so that the users aren't directly involved.
Secondly, for the application server migration to GCP VMs, you can use GCP managed keys for the VMs. It's simple and effective. There is no need to provision and manage keys (User- managed keys) by yourself for the VMs.
GCP-managed keys are used by Cloud Platform services such as App Engine and Compute Engine. These keys cannot be downloaded. Google will keep the keys and automatically rotate them on an approximately weekly basis.
C) is incorrect in the solution for on-premises access to GCP Datastore - This is possible options that might require more setup than worthwhile for the requirements.
D) is incorrect for reason of application server migration: you can use GCP managed keys for the VMs. It's simple and effective. There is no need to provision and manage keys (User-managed keys) by yourself for the application VMs

 

NEW QUESTION 55
You are managing an application deployed on Cloud Run for Anthos, and you need to define a strategy for deploying new versions of the application. You want to evaluate the new code with a subset of production traffic to decide whether to proceed with the rollout. What should you do?

A. Deploy a new revision to Cloud Run with the new version. Configure traffic percentage between revisions.B. In the Google Cloud Console page for Cloud Run, set up continuous deployment using Cloud Build for the development branch. As part of the Cloud Build trigger, configure the substitution variable TRAFFIC_PERCENTAGE with the percentage of traffic you want directed to a new version.C. In the Google Cloud Console, configure Traffic Director with a new Service that points to the new version of the application on Cloud Run. Configure Traffic Director to send a small percentage of traffic to the new version of the application.D. Deploy a new service to Cloud Run with the new version. Add a Cloud Load Balancing instance in front of both services.

Answer: A

 

NEW QUESTION 56
The current Dress4win system architecture has high latency to some customers because it is located in one data center.
As of a future evaluation and optimizing for performance in the cloud, Dresss4win wants to distribute it's system architecture to multiple locations when Google cloud platform.
Which approach should they use?

A. Use a global load balancer with a set of virtual machines that forward the requests to a closer group of virtual machines managed by your operations team.B. Use a global load balancer with a set of virtual machines that forward the requests to a closer group of virtual machines as part of a separate managed instance groups.C. Use regional managed instance groups and a global load balancer to increase performance because the regional managed instance group can grow instances in each region separately based on traffic.D. Use regional managed instance groups and a global load balancer to increase reliability by providing automatic failover between zones in different regions.

Answer: B

 

NEW QUESTION 57
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