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NEW QUESTION: 1
한 회사 CFO는 최근 회사의 AWS 월별 청구서를 분석하고 사용중인 AWS Elastic Beanstalk 환경의 비용을 줄일 수있는 기회를 확인했습니다. CFO는 솔루션 아키텍트에게 아침에 Elastic Beanstalk 환경을 가동하고 하루가 끝나면 종료하는 고 가용성 솔루션을 설계하도록 요청했습니다.
솔루션은 운영 오버 헤드를 최소화하고 비용을 최소화하도록 설계되어야 합니다. 또한 여러 팀간에 증가하는 Elastic Beanstalk 환경 사용을 처리 할 수 있어야 하며 운영 비용을 낮게 유지하기 위해 모든 팀에 원 스톱 스케줄러 솔루션을 제공해야 합니다.
이러한 요구 사항을 충족하는 디자인은 무엇입니까?
A. Elastic Beanstalk 환경을 시작 및 중지하는 AWS Lambda 함수를 개발합니다. Elastic Beanstalk 환경 시작 / 중지 권한을 부여하는 Lambda 실행 역할을 구성하고 Lambda 함수에 역할을 할당합니다. Lambda 함수를 트리거하도록 cron 표현식 Amazon CloudWatch Events 규칙을 구성합니다.
B. 시간 기반 Auto Scaling 그룹을 구성합니다. 아침에 Auto Scaling 그룹이 Amazon EC2 인스턴스를 확장하고 EC2 인스턴스 사용자 날짜에 Elastic Beanstalk 환경 시작 명령을 입력하도록 합니다. 하루가 끝나면 인스턴스 번호를 0으로 축소하여 EC2 인스턴스를 종료합니다.
https://aws.amazon.com/premiumsupport/knowledge-center/schedule-elastic-beanstalk-stop-restart/
C. Linux EC2 Micro 인스턴스를 설정합니다. Elastic Beanstalk 환경의 시작 및 중지를 허용하도록 IAM 역할을 구성하고 인스턴스에 연결합니다. 인스턴스에서 스크립트를 생성하여 Elastic Beanstalk 환경을 시작 및 중지합니다. 스크립트를 실행하도록 인스턴스에서 크론 작업을 구성합니다.
D. 시작 및 중지 시간을 제어하기 위해 유형이 "wait"인 AWS Step Functions 상태 머신을 개발합니다. 활동 작업을 사용하여 Elastic Beanstalk 환경을 시작하고 중지합니다. Elastic Beanstalk 환경을 시작 및 중지 할 수 있도록 Step Functions에 대한 역할을 생성합니다. 매일 Step Functions를 호출하십시오.
Answer: C
NEW QUESTION: 2
MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
Scale and harden their PoC to support significantly more data flows generated when they ramp to more
than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control
topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production
- to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
Scale up their production environment with minimal cost, instantiating resources when and where
needed in an unpredictable, distributed telecom user community.
Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
Provide reliable and timely access to data for analysis from distributed research workers
Maintain isolated environments that support rapid iteration of their machine-learning models without
affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data
Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
100m records/day
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
MJTelco is building a custom interface to share data. They have these requirements:
1. They need to do aggregations over their petabyte-scale datasets.
2. They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?
A. Cloud Datastore and Cloud Bigtable
B. BigQuery and Cloud Bigtable
C. BigQuery and Cloud Storage
D. Cloud Bigtable and Cloud SQL
Answer: B
NEW QUESTION: 3
Which VNX Local Protection Suite offering uses journaling to track the changes?
A. VNX Replicator
B. RecoverPoint CRR
C. VNX Snapshots
D. RecoverPoint CDP
Answer: D
NEW QUESTION: 4
What are two examples of active, malicious attacks possible in IP Storage transport environments? (Choose two)
A. Identity spoofing
B. Data origin authentication
C. Man-in-the-middle
D. Eavesdropping
Answer: A,C
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