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NEW QUESTION: 1
A. Option C
B. Option B
C. Option D
D. Option A
Answer: B
NEW QUESTION: 2
무선 서비스를 제공하는 회사는 사용자 활동에 대한 로그 파일을 저장하고 분석하는 솔루션이 필요합니다.
현재 로그 파일은 Amazon EC2 인스턴스의 Amazon Linux에 매일 제공됩니다. 배치 스크립트는 하루에 한 번 실행되어 타사 도구에서 분석하는 데 사용되는 데이터를 집계합니다. 타사 도구로 푸시 된 데이터는 최종 사용자를위한 시각화를 생성하는 데 사용됩니다. 배치 스크립트는 유지 관리가 번거롭고 계속 증가하는 데이터 볼륨을 타사 도구에 제공하는 데 몇 시간이 걸립니다. 이 회사는 비용 절감을 원하며 개발 노력을 최소화하고 관리 오버 헤드를 줄이는 새로운 도구를 고려할 수 있습니다. 이 회사는 최소한의 오버 헤드로 분석을 거의 실시간으로 저장하고 수행 할 수있는보다 민첩한 솔루션을 구축하려고합니다. 이 솔루션은 회사의 최종 사용자 기반 성장을 충족시키기 위해 비용 효율적이고 확장 가능해야 합니다.
어떤 솔루션이 회사의 요구 사항을 충족합니까?
A. Auto Scaling 그룹의 EC2 인스턴스에서 실행중인 Amazon Kinesis 에이전트를 사용하여 데이터를 수집하여 Amazon Kinesis Data Forehose 전송 스트림으로 보냅니다. Kinesis Data Firehose 전송 스트림은 데이터를 Amazon ES로 직접 전달합니다. Kibana를 사용하여 데이터를 시각화하십시오.
B. EC2 인스턴스에서 실행중인 Amazon Kinesis 에이전트를 사용하여 데이터를 수집하여 Amazon Kinesis Data Firehose 전송 스트림으로 보냅니다. Kinesis Data Firehose 전송 스트림은 데이터를 Amazon S3에 전달합니다. AWS Lambda 함수를 사용하여 Amazon S3에서 Amazon ES로 데이터를 전달하십시오. Kibana를 사용하여 데이터를 시각화하십시오.
C. Amazon EC2의 데이터를 실시간으로 실패하고 Amazon S3에 데이터를 저장하는 Python 스크립트를 개발하십시오. copy 명령을 사용하여 Amazon S3에서 Amazon Redshift로 데이터를 복사하십시오. Amazon EC2에서 실행되는 비즈니스 인텔리전스 도구를 Amazon Redshift에 연결하고 시각화를 만듭니다.
D. Amazon EBS 최적화 EC2 인스턴스에서 실행되는 메모리 내 캐싱 애플리케이션을 사용하여 거의 실시간으로 로그 데이터를 캡처하십시오. 동일한 EC2 인스턴스에 Amazon ES 클러스터를 설치하여 거의 실시간으로 Amazon EC2에 제공되는 로그 파일을 저장합니다. Kibana 플러그인을 설치하여 시각화를 작성하십시오.
Answer: A
Explanation:
https://docs.aws.amazon.com/firehose/latest/dev/writing-with-agents.html A: Not real time.
C: Not sure if in-memory caching will help in terms of logs and not DB? Anyway installing ES cluster on the same EC2 instance means the ES is not managed and will increase overhead.
D: ES already has direct integration with Firehose, there is no need to transfer to S3 first unless it needs to retain the data for long period of time for analysis
https://aws.amazon.com/elasticsearch-service/?nc=sn&loc=1
NEW QUESTION: 3
A. Option B
B. Option C
C. Option D
D. Option A
Answer: C