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NEW QUESTION: 1
회사에는 매일 1TB의 상태 경고를 총체적으로 생성하는 수천 개의 에지 장치가 있습니다. 각 경고의 크기는 약 2KB입니다. 솔루션 설계자는 향후 분석을 위해 경고를 수집하고 저장하는 솔루션을 구현해야 합니다.
회사는 고 가용성 솔루션을 원하지만 회사는 비용을 최소화 해야 하고 추가 인프라를 관리하고 싶지 않습니다. 또한 회사는 즉각적인 분석을 위해 14 일 동안의 데이터를 보관하고 14 일이 지난 모든 데이터를 보관하기를 원합니다. 이러한 요구 사항을 충족하는 효율적인 솔루션?
A. Amazon Kinesis Data Firehose 전송 스트림을 생성하여 경고를 수집합니다. Amazon Elasticsearch Service (Amazon ES) 클러스터에 경고를 전송하도록 Kinesis Data Firehose 스트림을 구성합니다. 매일 수동 스냅 샷을 생성하고 삭제하도록 Amazon ES 클러스터를 설정합니다. 다음보다 오래된 클러스터의 데이터
14 일
B. Amazon Kinesis Data Firehose 전송 스트림을 생성하여 알림을 수집합니다. Amazon S3 버킷에 알림을 전송하도록 Kinesis Data Firehose 스트림을 구성합니다. 14 일 후에 데이터를 Amazon S3 Glacier로 전환하도록 S3 LifecycJe 구성을 설정합니다.
C. Amazon Simple Queue Service (Amazon SQS) 표준 대기열을 생성하여 알림을 수집하고 메시지 보존 기간을 14 일로 설정합니다. SQS 대기열을 폴링하도록 소비자를 구성하여 메시지 수명을 확인하고 필요한 경우 메시지 데이터를 분석합니다. 메시지가 14 일이 지난 경우 소비자는 메시지를 Amazon S3 버킷에 복사하고 SQS 대기열에서 메시지를 삭제해야 합니다.
D. 두 가용 영역에서 Amazon EC2 인스턴스를 시작하고 Elastic Load Balancer 뒤에 배치하여 경고를 수집합니다. Amazon S3 버킷에 경고를 저장할 EC2 인스턴스에 스크립트 생성 데이터를 전환하도록 S3 수명주기 구성 설정 14 일 후 Amazon S3 Glacier로
Answer: C

NEW QUESTION: 2
If a component does NOT appear in the Sidekick, how could an AEM author cause it to appear? (Choose the
best answer.)
A. Switch to Design mode in the Sidekick and add a component to the paragraph system
B. Switch to Preview mode in the Sidekick
C. Contact an AEM developer
D. Edit the Client Context cloud
Answer: A

NEW QUESTION: 3
A large insurance company wants to buy a new claims processing system or upgrade one of its two existing system. Each year the claims department is given a $3.5 million budget to spend. Time is of the essence since there are tome regulatory charges that will be coming the following year that will require several features that currently neither one of the two claims System currently support.
There are eight stakeholders involved in this initiative. There are local to where the claim system are managed, while five are located across the country. The business analyst (BA) struggled to get all stakeholders to agree on the desired features but ultimately got agreement on ten identified key features for the new claims system.
The BA was able to build a current state and future state process model which included all ten key features.
System a processes 75% of the company's claims. It is 5 years old and the claim processors love it because it is easy to use. However it must go offline for two hours each day. The code is very module so it does have flexibility to be modified. To upgrade system A to have all ten features it would cost $5 million. System A would be at capacity if it were to process all of the company's claims.
System B process 25% of the company's claims. It is an older mainframe system, but rarely goes offline. It could easily handle double the number of claims that system A processes. However it has a lot of legacy code and would cost $6 million to upgrade.
Both system have some of the tem desired key features. But neither system has all ten. The cost to buy a new system would be $7 million.
Below is the estimated cost for each feature in priority order.

If System A is unanimously selected to be the system for upgrade but the technical lead says that System A will have to be taken offline for 3 months, what kind of strategy should the project team develop while system A is offline?
A. Organizational
B. Change
C. Competitive
D. Stakeholder
Answer: B

NEW QUESTION: 4
A photo-sharing and publishing company receives 10,000 to 150,000 images daily. The company receives the images from multiple suppliers and users registered with the service. The company is moving to AWS and wants to enrich the existing metadata by adding data using Amazon Rekognition.
The following is an example of the additional data:

As part of the cloud migration program, the company uploaded existing image data to Amazon S3 and told users to upload images directly to Amazon S3.
What should the Solutions Architect do to support these requirements?
A. Trigger AWS Lambda based on an S3 event notification to create additional metadata using Amazon Rekognition. Use Amazon DynamoDB to store the metadata and Amazon ES to create an index. Use a web front-end to provide search capabilities backed by Amazon ES.
B. Start an Amazon SQS queue based on S3 event notifications. Then have Amazon SQS send the metadata information to Amazon DynamoDB. An application running on Amazon EC2 extracts data from Amazon Rekognition using the API and adds data to DynamoDB and Amazon ES. Use a web front-end to provide search capabilities backed by Amazon ES.
C. Use Amazon Kinesis to stream data based on an S3 event. Use an application running in Amazon EC2 to extract metadata from the images. Then store the data on Amazon DynamoDB and Amazon CloudSearch and create an index. Use a web front-end with search capabilities backed by CloudSearch.
D. Trigger AWS Lambda based on an S3 event notification to create additional metadata using Amazon Rekognition. Use Amazon RDS MySQL Multi-AZ to store the metadata information and use Lambda to create an index. Use a web front-end with search capabilities backed by Lambda.
Answer: A
Explanation:
Explanation
https://github.com/aws-samples/lambda-refarch-imagerecognition