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NEW QUESTION: 1
Case Study: 6 - TerramEarth
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.
For this question, refer to the TerramEarth case study. To be compliant with European GDPR regulation, TerramEarth is required to delete data generated from its European customers after a period of 36 months when it contains personal data. In the new architecture, this data will be stored in both Cloud Storage and BigQuery. What should you do?
A. Create a BigQuery time-partitioned table for the European data, and set the partition expiration period to 36 months. For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.
B. Create a BigQuery time-partitioned table for the European data, and set the partition period to 36 months. For Cloud Storage, use gsutil to create a SetStorageClass to NONE action with an Age condition of 36 months.
C. Create a BigQuery table for the European data, and set the table retention period to 36 months.
For Cloud Storage, use gsutil to enable lifecycle management using a DELETE action with an Age condition of 36 months.
D. Create a BigQuery table for the European data, and set the table retention period to 36 months.
For Cloud Storage, use gsutil to create a SetStorageClass to NONE action when with an Age condition of 36 months.
Answer: A

NEW QUESTION: 2
조직에는 매일 10GB의 원격 측정 데이터를 생성하는 10,000 개의 장치가 있으며 각 레코드 크기는 약 10KB입니다. 각 레코드에는 100 개의 필드가 있으며 하나의 필드는 영어로 된 "문자열"데이터 유형의 비정형 로그 데이터로 구성됩니다. 일부 필드는 실시간 대시 보드에 필요하지만 모든 필드는 장기 생성에 사용할 수 있어야 합니다.
또한 조직은 한 달 안에 AWS로 마이그레이션 해야 하는 SAN에서 이전에 정리되고 구조화 된 데이터를 날짜별로 분할 한 10PB를 가지고 있습니다. 현재 조직에는 솔루션에 실시간 기능이 없습니다. 온-프레미스 데이터웨어 하우스의 스토리지 제한으로 인해 시각화를 위해 JDBC를 통해 ANSI SQL 쿼리로 장기 추세를 생성하는 동안 선택적 데이터가 로드됩니다. 조직은 일회성 데이터로드 외에도 비용 효율적이고 실시간 솔루션이 필요합니다.
이러한 요구 사항을 어떻게 충족시킬 수 있습니까? (두 가지를 선택하십시오.)
A. AWS IoT를 사용하여 디바이스에서 Amazon SQS 대기열로 데이터를 전송하고, Auto Scaling 그룹에서 작업자 세트를 생성하고, 대기열에서 배치로 레코드를 읽어 데이터를 처리하고 저장합니다. 요청 데이터 세트를 필터링하고 실시간 분석을 위해 Amazon Elasticsearch Service에 저장하려면 AWS Lambda 함수가 연결된 Amazon SNS 대기열로 팬 아웃하십시오.
B. AWS와 온 프레미스 데이터 센터간에 Direct Connect 연결을 생성하고 S3 Acceleration을 사용하여 데이터를 Amazon S3에 복사하십시오. Amazon Athena를 사용하여 데이터를 쿼리하십시오.
C. IoT 규칙 엔진을 사용하여 AWS IoT를 사용하여 디바이스의 데이터를 Amazon Kinesis Data Streams로 보냅니다. Kinesis 스트림에 첨부 된 하나의 Kinesis Data Firehose 스트림을 사용하여 날짜별로 파티션 된 Amazon S3 버킷으로 데이터를 스트리밍하십시오. 동일한 Kinesis 스트림과 함께 AWS Lambda 함수를 연결하여 실시간 분석을 위해 Amazon DynamoDB에 수집하는 데 필요한 필드를 필터링하십시오.
D. IoT 규칙 엔진을 사용하여 AWS IoT를 사용하여 디바이스에서 Amazon Kinesis Data Streams로 데이터를 보냅니다. Kinesis 스트림에 연결된 하나의 Kinesis Data Firehose 스트림을 사용하여 날짜별로 분할 된 데이터를 일괄 처리하고 스트리밍합니다. 동일한 Kinesis 스트림에 연결된 다른 Kinesis Firehose 스트림을 사용하여 실시간 분석을 위해 Elasticsearch로 수집하는 데 필요한 필드를 필터링하십시오.
E. 여러 AWS Snowball Edge 디바이스를 사용하여 데이터를 Amazon S3로 전송하고 Amazon Athena를 사용하여 데이터를 쿼리합니다.
Answer: A,C

NEW QUESTION: 3
Clients using Smarter Analytics are able to transition data into better business decisions to drive better outcomes.
How does IBM Business Analytics summarize the process to drive better outcomes from data?
A. Automate - Animate - Actuate
B. Anticipate - Automate - Authorize
C. Aim - Allow - Authorize
D. Align - Anticipate - Act
Answer: D
Explanation:
Reference:https://www-304.ibm.com/connections/blogs/bcde08b8-816c-42a8-aa375f1ce02470a9/entry/march_22_2012_11_16_am34?lang=en_us