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NEW QUESTION: 1
A. Option E
B. Option C
C. Option B
D. Option A
E. Option D
Answer: D
NEW QUESTION: 2
회사는 온 프레미스 데이터 센터에 HPC (High Performance Computing) 클러스터를 보유하고 있으며,이 시스템은 매달 1 주일 동안 수천 개의 작업을 병렬로 실행하여 페타 바이트의 이미지를 처리합니다. 이미지는 네트워크 파일 서버에 저장되며 재해 복구 사이트로 복제됩니다. 온-프레미스 데이터 센터가 용량에 도달하여 클러스터를 더 잘 활용하기 위해 한 달 동안 작업을 분산시키기 시작하여 작업 완료가 지연되었습니다.
이 회사는 솔루션 아키텍트에게 현재 5,000 개의 코어 용량과 10 페타 바이트의 데이터 용량을 넘어서 확장 할 수있는 AWS에서 비용 효율적인 솔루션을 설계하도록 요청했습니다. 솔루션에는 최소한의 관리 오버 헤드가 필요하고 현재 내구성 수준을 유지해야 합니다.
회사의 요구 사항을 충족시키는 솔루션은 무엇입니까?
A. 처리해야 할 작업을 대기열에 추가하기 위해 처리 할 작업 목록을 Amazon SQS에 제출합니다. 스팟 집합을 사용하여 대기열 깊이에 따라 자동으로 확장되는 다양한 Amazon EC2 작업자 인스턴스 클러스터를 생성합니다. Amazon EFS를 사용하여 클러스터의 모든 인스턴스에서 공유하는 모든 데이터를 저장하십시오.
B. 원시 데이터를 Amazon S3에 저장하고 관리 형 컴퓨팅 환경과 함께 AWS Batch를 사용하여 스팟 집합을 생성합니다. 작업을 AWS Batch Job Queues에 제출하여 Amazon S3에서 Amazon EBS 볼륨으로 객체를 가져와 임시 스토리지를 처리 한 다음 결과를 Amazon S3에 다시 씁니다.
C. 작업의 실행 파일을 사용하여 Amazon Elastic Container Registry에 컨테이너를 만듭니다.
Auto Scaling 그룹에서 스팟 집합과 함께 Amazon ECS를 사용하십시오. 원시 데이터를 Amazon EBS SC1 볼륨에 저장하고 출력을 Amazon S3에 씁니다.
D. Spark를 사용하여 Amazon S3에서 데이터를 가져 오는 온 디맨드 인스턴스와 예약 인스턴스 작업 노드의 조합으로 Amazon EMR 클러스터를 생성합니다. Amazon EMR 클러스터에서 처리해야하는 작업 목록을 유지하려면 Amazon DynamoDB를 사용하십시오.
Answer: A
NEW QUESTION: 3
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
