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NEW QUESTION: 1

A. Option B
B. Option C
C. Option D
D. Option A
Answer: C

NEW QUESTION: 2
What is the overall score range that a business application could have?
A. 1 through 10
B. 1 through 100
C. 1 through 1000
D. 1 through 10000
Answer: A

NEW QUESTION: 3
한 회사에 20PB HDFS 데이터베이스가 있는 대규모 온 프레미스 Apache Hadoop 클러스터가 있습니다. 클러스터는 약 200 개의 인스턴스와 1PB 씩 분기마다 증가하고 있습니다. 회사의 목표는 Hadoop 데이터의 복원력을 활성화하고 클러스터 노드 손실의 영향을 제한하며 비용을 크게 줄이는 것입니다. 현재 클러스터는 연중 무휴 24 시간 실행되며 대화 형 쿼리 및 일괄 처리를 포함한 다양한 분석 워크로드를 지원합니다.
최소한의 비용과 가동 중지 시간으로 이러한 요구 사항을 충족하는 솔루션은 무엇입니까?
A. AWS Snowmobile을 사용하여 기존 클러스터 데이터를 Amazon S3로 마이그레이션 합니다. 현재 클러스터와 크기 및 구성이 유사한 영구 Amazon EMR 클러스터를 생성합니다. EMRFS에 데이터를 저장합니다. 예약 인스턴스를 사용하여 비용을 최소화합니다. 워크로드가 분기마다 증가함에 따라 추가 예약 인스턴스를 구매하고 클러스터에 추가하십시오.
B. AWS Snowball을 사용하여 기존 클러스터 데이터를 Amazon S3로 마이그레이션합니다. 온 프레미스 클러스터의 기록 데이터를 기반으로 대화 형 워크로드를 처리하도록 초기에 크기가 조정 된 영구 Amazon EMR 클러스터를 생성합니다. EMRFS에 저장하십시오. 마스터 및 코어 노드 용 예약 인스턴스와 작업 노드 용 스팟 인스턴스, Amazon CloudWatch 지표를 기반으로 작업 노드 자동 확장을 사용하여 비용을 최소화합니다. 유사하게 최적화 된 배치 워크로드를 위해 작업별로 최적화 된 클러스터를 생성합니다.
C. AWS Direct Connect를 사용하여 기존 클러스터 데이터를 Amazon S3로 마이그레이션합니다. 온 프레미스 클러스터의 기록 데이터를 기반으로 대화 형 워크로드를 처리하도록 초기에 크기가 조정 된 영구 Amazon EMR 클러스터를 생성합니다. EMRFS에 데이터를 저장합니다. 마스터 및 코어 노드 용 예약 인스턴스와 작업 노드 용 스팟 인스턴스, Amazon CloudWatch 지표를 기반으로 작업 노드 자동 확장을 사용하여 비용을 최소화합니다. 유사하게 최적화 된 배치 워크로드를 위해 작업별로 최적화 된 클러스터를 생성합니다.
Q : Snowmobile과 Snowball 중에서 어떻게 선택해야 합니까?
단일 위치에서 10PB 이상의 대규모 데이터 세트를 마이그레이션 하려면 Snowmobile을 사용해야 합니다. 10PB 미만이거나 여러 위치에 배포 된 데이터 세트의 경우 Snowball을 사용해야 합니다. 또한 네트워크 백본에서 사용 가능한 대역폭의 양을 평가해야 합니다. 수백 Gb / s의 예비 처리량을 가진 고속 백본이 있는 경우 Snowmobile을 사용하여 대규모 데이터 세트를 한 번에 마이그레이션 할 수 있습니다. 백본에 제한된 대역폭이 있는 경우 여러 Snowball을 사용하여 데이터를 점진적으로 마이그레이션하는 것을 고려해야 합니다.
D. AWS Snowmobile을 사용하여 기존 클러스터 데이터를 Amazon S3로 마이그레이션합니다. 온 프레미스 클러스터의 기록 데이터를 기반으로 대화 형 워크로드를 처리하도록 초기에 크기가 조정 된 영구 Amazon EMR 클러스터를 생성합니다. EMRFS에 데이터를 저장합니다. 마스터 및 코어 노드 용 예약 인스턴스와 작업 노드 용 스팟 인스턴스, Amazon CloudWatch 지표를 기반으로 작업 노드 자동 확장을 사용하여 비용을 최소화합니다. 유사하게 최적화 된 배치 워크로드를 위해 작업별로 최적화 된 클러스터를 생성합니다.
Answer: D