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NEW QUESTION: 1
You are setting up a new share for your data that backs up to an AltaVault appliance.
In this situation, what are three configurable parameters available for share optimization? (Choose three.)
A. early eviction
B. data pinning
C. deduplication
D. archive mode
E. data replication
Answer: A,B,C
NEW QUESTION: 2
Which R71 SmartConsole tool would you use to verify the installed Security Policy name on a Security Gateway?
A. SmartView Monitor
B. SmartView Status
C. None, SmartConsole applications only communicate with the Security Management Server.
D. SmartUpdate
Answer: A
NEW QUESTION: 3
회사에는 20 PB HDFS 데이터베이스가있는 대규모 온-프레미스 Apache Hadoop 클러스터가 있습니다. 클러스터는 분기마다 약 200 개의 인스턴스와 1PB 씩 증가하고 있습니다. 이 회사의 목표는 Hadoop 데이터의 복원력을 활성화하고 클러스터 노드 손실로 인한 영향을 제한하며 비용을 크게 줄이는 것입니다. 현재 클러스터가 실행됩니다
연중 무휴 24 시간 대화식 쿼리 및 일괄 처리를 포함한 다양한 분석 워크로드를 지원합니다.
가장 적은 비용과 가동 중지 시간으로 이러한 요구 사항을 충족하는 솔루션은 무엇입니까?
A. AWS Snowmobile을 사용하여 기존 클러스터 데이터를 Amazon S3로 마이그레이션하십시오. 현재 클러스터와 비슷한 크기 및 구성의 영구 Amazon EMR 클러스터를 생성합니다. EMRFS에 데이터를 저장하십시오.
예약 인스턴스를 사용하여 비용을 최소화하십시오. 분기마다 워크로드가 증가함에 따라 추가 예약 인스턴스를 구매하고 클러스터에 추가하십시오.
B. AWS Direct Connect를 사용하여 기존 클러스터 데이터를 Amazon S3로 마이그레이션하십시오. 온 프레미스 클러스터의 히스토리 데이터를 기반으로 대화식 워크로드를 처리하도록 초기 크기의 영구 Amazon EMR 클러스터를 생성합니다. EMRFS에 데이터를 저장하십시오. 마스터 및 코어 노드 용 예약 인스턴스 및 작업 노드 용 스팟 인스턴스를 사용하고 Amazon CloudWatch 지표를 기반으로 자동 스케일 작업 노드를 사용하여 비용을 최소화하십시오. 비슷하게 최적화 된 배치 워크로드를 위해 작업별로 최적화 된 클러스터를 만듭니다.
C. AWS Snowball을 사용하여 기존 클러스터 데이터를 Amazon S3로 마이그레이션하십시오. 온 프레미스 클러스터의 히스토리 데이터를 기반으로 대화식 워크로드를 처리하도록 초기 크기의 영구 Amazon EMR 클러스터를 생성합니다. EMRFS에 저장하십시오. 마스터 및 코어 노드 용 예약 인스턴스 및 작업 노드 용 스팟 인스턴스를 사용하고 Amazon CloudWatch 지표를 기반으로 자동 스케일 작업 노드를 사용하여 비용을 최소화하십시오. 비슷하게 최적화 된 배치 워크로드를 위해 작업별로 최적화 된 클러스터를 만듭니다.
D. AWS Snowmobile을 사용하여 기존 클러스터 데이터를 Amazon S3로 마이그레이션하십시오. 온 프레미스 클러스터의 히스토리 데이터를 기반으로 대화식 워크로드를 처리하도록 초기 크기의 영구 Amazon EMR 클러스터를 생성합니다. EMRFS에 데이터를 저장하십시오. 마스터 및 코어 노드 용 예약 인스턴스 및 작업 노드 용 스팟 인스턴스를 사용하고 Amazon CloudWatch 지표를 기반으로 자동 스케일 작업 노드를 사용하여 비용을 최소화하십시오. 비슷하게 최적화 된 배치 워크로드를 위해 작업별로 최적화 된 클러스터를 만듭니다.
Answer: D
Explanation:
Explanation
Q: How should I choose between Snowmobile and Snowball?
To migrate large datasets of 10PB or more in a single location, you should use Snowmobile. For datasets less than 10PB or distributed in multiple locations, you should use Snowball. In addition, you should evaluate the amount of available bandwidth in your network backbone. If you have a high speed backbone with hundreds of Gb/s of spare throughput, then you can use Snowmobile to migrate the large datasets all at once. If you have limited bandwidth on your backbone, you should consider using multiple Snowballs to migrate the data incrementally.
