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NEW QUESTION: 1
You are designing a Citrix XenServer based virtualization solution that runs on ProLiant
Gen9 servers. Which design parameters should you take into consideration as part of your solution? (Select two.)
A. memory consumed by the Control Domain on the host
B. FCoE dedicated network for XenServer migrations
C. NVDIMMS configured as caching modules for the host OS installation
D. memory consumed by the XenServer crash kernel
E. 35 percent additional hardware resources for host server overhead
Answer: A,B

NEW QUESTION: 2
When the maintenance engineer arrives at the customer site, the working environment around the site should be recorded. The main collection items are (). (Multiple choice)
A. Equipment installation distance
B. If there is a bad environment around it (such as dust or moisture)
C. Ambient temperature
D. Ventilation
Answer: A,B,C,D

NEW QUESTION: 3
Identify twocorrectstatements aboutASN creation.
A. Oracle WMS Cloud allows creation of a Single/multiple ASN from a PO.
B. Oracle WMS Cloud only allows creation of a single ASN from a PO.
C. The ASN number is always auto-generated from a sequence number.
D. The ASN number can be system generated or manually entered.
Answer: B,D

NEW QUESTION: 4
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 활성화 하고 클러스터 노드 손실의 영향을 제한하며 비용을 크게 줄이는 것입니다. 현재 클러스터가 실행됩니다.
24/7 및 대화 형 쿼리 및 일괄 처리를 포함한 다양한 분석 워크로드를 지원합니다.
최소한의 비용과 가동 중지 시간으로 이러한 요구 사항을 충족하는 솔루션은 무엇입니까?
A. 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.
B. 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.
C. AWS Snowmobile을 사용하여 기존 클러스터 데이터를 Amazon S3로 마이그레이션 합니다. 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.
D. 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.
Answer: C
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.