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NEW QUESTION: 1
Given the following data set WORK.DM:
Which subjects will appear in the LOG file?
A. 001 and 006
B. 004 and 006
C. 003 and 004
D. 001 and 004
Answer: B
NEW QUESTION: 2
A Siebel iHelp item can:
A. navigate the user to the correct view
B. highlight list columns or controls used in a step
C. show reference material from Siebel Bookshelf
D. enter default field values
E. suggest a value for a field
F. include branching logic
Answer: A
Explanation:
Reference: http://docs.oracle.com/cd/B40099_02/books/AppsAdmin/AppsAdminiHelp6.html (Topic: To create an ihelp step, bulleted point #1)
NEW QUESTION: 3
회사에는 20 PB HDFS 데이터베이스가있는 대규모 사내 구축 형 Apache Hadoop 클러스터가 있습니다. 클러스터는 매 분기마다 대략 200 인스턴스 및 1 PB 씩 증가합니다. 이 회사의 목표는 Hadoop 데이터의 복원력을 높이고 클러스터 노드 손실의 영향을 최소화하며 비용을 크게 절감하는 것입니다. 현재 클러스터가 실행됩니다.
대화 형 쿼리 및 일괄 처리를 포함한 다양한 분석 작업 부하를 지원합니다.
어떤 솔루션이 최소한의 비용과 다운 타임으로 이러한 요구 사항을 충족시킬 수 있습니까?
A. AWS Snowball을 사용하여 기존 클러스터 데이터를 Amazon S3로 마이그레이션합니다. 온 - 프레미스 클러스터의 내역 데이터를 기반으로 대화식 작업 부하를 처리 할 수있는 크기로 영구적 인 Amazon EMR 클러스터를 만듭니다. EMRFS에 저장하십시오. 마스터 노드 및 코어 노드의 예약 인스턴스 및 태스크 노드의 스팟 인스턴스를 사용하여 비용을 최소화하고 Amazon CloudWatch 메트릭을 기반으로 태스크 노드를 자동 조정합니다. 유사하게 최적화 된 배치 작업 부하에 대해 작업 별 최적화 클러스터를 생성합니다.
B. AWS Snowmobile을 사용하여 기존 클러스터 데이터를 Amazon S3로 마이그레이션합니다. 현재 클러스터와 비슷한 크기 및 구성의 영구적 인 Amazon EMR 클러스터를 만듭니다. EMRFS에 데이터를 저장하십시오.
예약 인스턴스를 사용하여 비용을 최소화하십시오. 작업 부하가 매 분기 증가하면 예약 인스턴스를 추가로 구입하고 클러스터에 추가하십시오.
C. AWS Snowmobile을 사용하여 기존 클러스터 데이터를 Amazon S3로 마이그레이션합니다. 온 - 프레미스 클러스터의 기록 데이터를 기반으로 대화식 작업 부하를 처리 할 수있는 크기로 영구적 인 Amazon EMR 클러스터를 만듭니다. EMRFS에 데이터를 저장하십시오. 마스터 노드 및 코어 노드의 예약 인스턴스 및 태스크 노드의 스팟 인스턴스를 사용하여 비용을 최소화하고 Amazon CloudWatch 메트릭을 기반으로 태스크 노드를 자동 조정합니다. 유사하게 최적화 된 배치 작업 부하에 대해 작업 별 최적화 클러스터를 생성합니다.
D. AWS Direct Connect를 사용하여 기존 클러스터 데이터를 Amazon S3로 마이그레이션합니다. 온 - 프레미스 클러스터의 기록 데이터를 기반으로 대화식 작업 부하를 처리 할 수있는 크기로 영구적 인 Amazon EMR 클러스터를 만듭니다. EMRFS에 데이터를 저장하십시오. 마스터 노드 및 코어 노드의 예약 인스턴스 및 태스크 노드의 스팟 인스턴스를 사용하여 비용을 최소화하고 Amazon CloudWatch 메트릭을 기반으로 태스크 노드를 자동 조정합니다. 유사하게 최적화 된 배치 작업 부하에 대해 작업 별 최적화 클러스터를 생성합니다.
Answer: A