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?
Correct Answer: D
Question 222
For this question, refer to the TerramEarth case study. TerramEarth has decided to store data files in Cloud Storage. You need to configure Cloud Storage lifecycle rule to store 1 year of data and minimize file storage cost. Which two actions should you take?
Correct Answer: B
Question 223
For this question, refer to the TerramEarth case study Your development team has created a structured API to retrieve vehicle dat a. They want to allow third parties to develop tools for dealerships that use this vehicle event data. You want to support delegated authorization against this data. What should you do?
Correct Answer: A
https://cloud.google.com/appengine/docs/flexible/go/authorizing-apps https://cloud.google.com/docs/enterprise/best-practices-for-enterprise-organizations#delegate_application_authorization_with_oauth2 Delegate application authorization with OAuth2 Cloud Platform APIs support OAuth 2.0, and scopes provide granular authorization over the methods that are supported. Cloud Platform supports both service-account and user-account OAuth, also called three-legged OAuth. Reference: https://cloud.google.com/appengine/docs/flexible/go/authorizing-apps
Question 224
For this question, refer to the Dress4Win case study. Dress4Win is expected to grow to 10 times its size in 1 year with a corresponding growth in data and traffic that mirrors the existing patterns of usage. The CIO has set the target of migrating production infrastructure to the cloud within the next 6 months. How will you configure the solution to scale for this growth without making major application changes and still maximize the ROI?
Correct Answer: D
Topic 5, TerramEarth Case 2 Company Overview TerramEarth manufactures heavy equipment for the mining and agricultural industries. About 80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive. Solution Concept There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second. Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced. The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules. Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second with 22 hours of operation per day, TerramEarth collects a total of about 9 TB/day from these connected vehicles. Existing Technical Environment TerramEarth's existing architecture is composed of Linux and Windows-based systems that reside in a single U.S. west coast based data center. These systems gzip CSV files from the field and upload via FTP, and place the data in their data warehouse. Because this process takes time, aggregated reports are based on data that is 3 weeks old. With this data, TerramEarth has been able to preemptively stock replacement parts and reduce unplanned downtime of their vehicles by 60%. However, because the data is stale, some customers are without their vehicles for up to 4 weeks while they wait for replacement parts. Business Requirements Decrease unplanned vehicle downtime to less than 1 week. Support the dealer network with more data on how their customers use their equipment to better position new products and services Have the ability to partner with different companies - especially with seed and fertilizer suppliers in the fast-growing agricultural business - to create compelling joint offerings for their customers. Technical Requirements Expand beyond a single datacenter to decrease latency to the American Midwest and east coast. Create a backup strategy. Increase security of data transfer from equipment to the datacenter. Improve data in the data warehouse. Use customer and equipment data to anticipate customer needs. Application 1: Data ingest A custom Python application reads uploaded datafiles from a single server, writes to the data warehouse. Compute: Windows Server 2008 R2 - 16 CPUs - 128 GB of RAM - 10 TB local HDD storage Application 2: Reporting An off the shelf application that business analysts use to run a daily report to see what equipment needs repair. Only 2 analysts of a team of 10 (5 west coast, 5 east coast) can connect to the reporting application at a time. Compute: Off the shelf application. License tied to number of physical CPUs - Windows Server 2008 R2 - 16 CPUs - 32 GB of RAM - 500 GB HDD Data warehouse: A single PostgreSQL server - RedHat Linux - 64 CPUs - 128 GB of RAM - 4x 6TB HDD in RAID 0 Executive Statement Our competitive advantage has always been in the manufacturing process, with our ability to build better vehicles for lower cost than our competitors. However, new products with different approaches are constantly being developed, and I'm concerned that we lack the skills to undergo the next wave of transformations in our industry. My goals are to build our skills while addressing immediate market needs through incremental innovations.
Question 225
For this question, refer to the Mountkirk Games case study. You need to analyze and define the technical architecture for the database workloads for your company, Mountkirk Games. Considering the business and technical requirements, what should you do?
