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NEW QUESTION: 1
회사에서 미디어 공유 애플리케이션을 구축 중이며 Amazon S3를 스토리지로 사용하기로 결정했습니다. 미디어 파일이 업로드되면 회사는 썸네일을 생성하고 이미지의 객체를 식별하며 비디오를 표준 형식 및 해상도로 트랜스 코딩하고 메타 데이터를 추출하여 Amazon DynamoDB 테이블에 저장하기 위해 다단계 프로세스를 시작합니다. 메타 데이터는 검색 및 탐색에 사용됩니다.
트래픽의 양은 가변적 임 솔루션은 불필요한 비용없이로드 급증을 처리하도록 확장 할 수 있어야 합니다.
솔루션 설계자가 이 워크로드를 지원하기 위해 권장해야 하는 것은 무엇입니까?
A. 객체가 S3 버킷에 저장된 경우 AWS Lambda 함수 트리거 Lambda 함수가 AWS Batch를 시작하여 객체 처리 단계를 수행하도록 합니다. 완료되면 객체 데이터를 DynamoDB 테이블에 배치합니다
B. 객체가 S3 버킷에 저장 될 때 AWS Step Functions 트리거 Step Functions에서 객체를 처리하는 데 필요한 단계를 수행한 다음 메타 데이터를 DynamoDB 테이블에 씁니다.
C. Amazon S3에 콘텐츠를 업로드하는 데 사용되는 웹 사이트 또는 모바일 앱으로 처리를 구축합니다. 객체가 업로드 될 때 필요한 데이터를 DynamoDB 테이블에 저장합니다.
D. 객체가 Amazon S3에 업로드 될 때 DynamoDB 테이블에 초기 항목을 저장하도록 AWS Lambda 함수를 트리거합니다. Auto Scaling 그룹의 Amazon EC2 인스턴스에서 실행중인 프로그램을 사용하여 처리되지 않은 인덱스를 폴링하여 처리를 수행하는 프로그램 사용
Answer: A

NEW QUESTION: 2

A. Publish-CsLISConfiguration
B. Publish-CsTopology
C. Export-CsLISconfiguration
D. Set-CsNetworkSubnet
Answer: A
Explanation:
References: https://technet.microsoft.com/en-us/library/gg398364.aspx

NEW QUESTION: 3
MapReduce v2 (MRv2/YARN) splits which major functions of the JobTracker into separate daemons? Select two.
A. Job coordination between the ResourceManager and NodeManager
B. Heath states checks (heartbeats)
C. Managing tasks
D. Launching tasks
E. Managing file system metadata
F. MapReduce metric reporting
G. Job scheduling/monitoring
H. Resource management
Answer: G,H
Explanation:
The fundamental idea of MRv2 is to split up the two major functionalities of
the JobTracker, resource management and job scheduling/monitoring, into separate
daemons. The idea is to have a global ResourceManager (RM) and per-application
ApplicationMaster (AM). An application is either a single job in the classical sense of Map-
Reduce jobs or a DAG of jobs.
Note:
The central goal of YARN is to clearly separate two things that are unfortunately smushed
together in current Hadoop, specifically in (mainly) JobTracker:
/ Monitoring the status of the cluster with respect to which nodes have which resources
available. Under YARN, this will be global.
/ Managing the parallelization execution of any specific job. Under YARN, this will be done separately for each job.
Reference: Apache Hadoop YARN - Concepts & Applications