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NEW QUESTION: 1
For this question, refer to the TerramEarth case study.
TerramEarth has equipped unconnected trucks with servers and sensors to collet telemetry data. Next year they want to use the data to train machine learning models. They want to store this data in the cloud while reducing costs. What should they do?
A. Have the vehicle' computer compress the data in hourly snapshots, and store it in a Google Cloud storage (GCS) Nearline bucket.
B. Push the telemetry data in Real-time to a streaming dataflow job that compresses the data, and store it in Google BigQuery.
C. Have the vehicle's computer compress the data in hourly snapshots, a Store it in a GCS Coldline bucket.
D. Push the telemetry data in real-time to a streaming dataflow job that compresses the data, and store it in Cloud Bigtable.
Answer: C
Explanation:
Topic 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
* 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
32GB of RAM
* Twin 250 GB HDD (RAID 1)
* 20 machines in US East Coast, each machine has:
Single dual-core CPU
2 4 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 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.

NEW QUESTION: 2
What is a consideration for a hardware administrator who is physically is physically installing multiple servers into a rack?
A. Ensure that the associated UPS can also power the top-of rack switch
B. Ensure that the server can connect to Windows updates
C. Avoid unevenly loading the rack and creating a hazard
D. Observe load requirements specified in document for the switch
Answer: D

NEW QUESTION: 3
회사는 매일 수천 개의 외부 콜백을 처리하는 응용 프로그램을 유지 관리합니다.
회사의 시스템 관리자는 롤링 기준으로 수신되는 콜백의 수를 알고 싶어하며이 데이터를 10 일 동안 사용할 수 있기를 원합니다. 또한 회사는 콜백 수가 정의 된 임계 값을 초과하는 경우 자동화 된 경고를 발행 할 수있는 기능을 원합니다.
통계를 추적하고 경고 할 필요성을 처리하는 가장 비용 효율적인 방법은 무엇입니까?
A. 콜백 데이터를 AWS X-Ray로 푸시하고 AWS 람다를 사용하여 초과 된 임계 값을 쿼리하고 표시하고 경고합니다.
B. 기록 데이터를 표시하고 초과 된 임계 값을 경고하기 위해 쿼리 할 수있는 콜백 데이터를 Amazon RDS 데이터베이스에 푸시합니다.
C. 콜백 데이터를 Amazon Kinesis Data Streams로 푸시하고 Amazon DynamoDB에 데이터를 저장하고 필요한 경고를 보내는 AWS Lambda 함수를 호출합니다.
D. 콜백 데이터를 사용자 정의 메트릭으로 Amazon CloudWatch에 푸시하고 CloudWatch 경고 메커니즘을 사용하여 시스템 관리자에게 알립니다.
Answer: C