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NEW QUESTION: 1
다음 중 핫 사이트에 대한 계약을 협상 할 때 가장 우려되는 것은 무엇입니까?
A. 재난 선포가 여러 번 발생하는 경우 현장 직원과 협력
B. 재난 선포가 여러 번 발생하는 경우 사이트 가용성
C. 복구 계획의 전체 테스트
D. 다른 조직과의 상호 합의
Answer: B
NEW QUESTION: 2
경매 웹 사이트를 통해 사용자는 수집 가능한 품목에 입찰 할 수 있습니다. 경매 규칙은 각 입찰가가 한 번만 처리되고 수신 된 순서대로 처리해야 합니다. 현재 구현은 입찰 기록을 Amazon Kinesis Data Streams에 기록하는 Amazon EC2 웹 서버의 함대를 기반으로 합니다. 단일 t2.large 인스턴스에는 입찰 프로세서를 실행하는 cron 작업이 있습니다.이 프로세서는 Kinesis Data Streams에서 들어오는 입찰을 읽고 각 입찰을 처리합니다. 경매 사이트가 인기를 얻고 있지만 사용자는 일부 입찰가가 등록되지 않는다고 불평하고 있습니다.
문제 해결은 수요가 많은 시간대에 입찰 프로세서가 너무 느리고 때때로 처리하는 동안 충돌하며 때로는 처리중인 레코드를 추적하지 못한다는 것을 나타냅니다.
입찰 처리가보다 안정적으로 이루어져야 합니다.
A. Kinesis Data Streams 대신 Amazon SNS 주제에 들어오는 각 입찰가를 게시하도록 웹 응용 프로그램을 리펙터링합니다. 사용자가 제출 한 즉시 각 입찰을 처리하는 AWS Lambda 기능을 트리거하도록 SNS 주제를 구성하십시오.
B. Kinesis Data Streams 대신 Amazon SQS FIFO 대기열에 들어오는 각 입찰을 게시하도록 웹 응용 프로그램을 리펙터링합니다. 입찰 프로세서가 SQS 큐를 계속 리 팩터링합니다. 최소 및 최대 크기가 1 인 자동 조정 그룹에 입찰 처리 EC2 인스턴스를 배치합니다.
C. EC2 인스턴스 유형을 t2.large에서 더 큰 일반 컴퓨팅 인스턴스 유형으로 전환합니다. Kinesis Data Streams의 IncomingRecords 메트릭을 기준으로 입찰 처리기를 실행하는 EC2 인스턴스의 수를 조정하는 자동 조정 그룹에 입찰 프로세서 EC2 인스턴스를 배치합니다.
D. Kinesis Data Streams에 입찰을 게시 할 때 Amazon Kinesis Producer Library (KPL)를 사용하도록 웹 응용 프로그램을 리 팩터합니다. 입찰가 프로세서를 리 팩터링하여 키네시스 데이터 스트림의 각 레코드에 읽지 않음, 처리 중 및 처리 중으로 플래그를 지정합니다. 각 입찰 처리가 실행될 때 처리되지 않은 레코드에 대해 Kinesis Data Stream을 스캔하십시오.
Answer: C
NEW QUESTION: 3
A. Dism
B. Mountvol
C. Device Manager
D. Server Manager
Answer: A
Explanation:
You can use the Deployment Image Servicing and Management (DISM) tool to mount a Windows image from a WIM or VHD file. Mounting an image maps the contents of the image to a directory so that you can service the image using DISM without booting into the image. You can also perform common file operations, such as copying, pasting, and editing on a mounted image.
To apply packages and updates to a Windows Embedded Standard 7 image, we recommend creating a configuration set and then using Deployment Imaging Servicing and Management (DISM) to install that configuration set. Although DISM can be used to install individual updates to an image, this method carries some additional risks and is not recommended.
NEW QUESTION: 4
두 개의 AZ에있는 여러 M3 인스턴스에서 실행되는 고객 대응 응용 프로그램이 있습니다. 이러한 인스턴스는 부하가 증가 할 때 확장되도록 구성된 자동 조정 그룹에 있습니다. CloudWatch 메트릭을 살펴본 후 매일 특정 시간 동안 자동 확장 그룹에 평상시보다 훨씬 많은 인스턴스가 있음을 알았습니다. 그럼에도 불구하고 고객 중 한 명은 매일 그 기간 동안 응용 프로그램이 매우 느리게 응답한다는 불평을합니다.
응용 프로그램은 400 개의 쓰기 용량 단위와 400 개의 읽기 용량 단위가있는 DynamoDB 테이블을 읽고 쓰는 중입니다. 기본 키는 회사 ID이며 테이블은 대략 저장합니다
20TB의 데이터 어느 솔루션이 확장 가능하고 비용 효율적인 방식으로 문제를 해결할 것입니까?
정답을 선택하십시오.
A. DynamoDB는이 사용 사례를위한 좋은 해결책이 아닙니다. 대신 데이터 파이프 라인을 만들어 DynamoDB에서 Amazon RDS로 데이터를 이동하십시오.
B. 데이터 파이프 라인을 사용하여 테이블 전체에 데이터 세트를 고르게 분산시키는 다른 기본 키를 사용하여 DynamoDB 테이블을 새로운 DynamoDB 테이블로 마이그레이션합니다
C. ElastiCache, Memcached 또는 Redis를 사용하여 웹 응용 프로그램 앞에 캐싱 계층 추가
D. 동일한 회사의 고객이 모두 동시에 테이블을 사용할 때 DynamoDB 테이블이 스로틀되기 때문에 읽기 및 쓰기 용량 단위의 수를 두 배로 늘립니다.
Answer: B
Explanation:
설명:
파편을 효율적으로 사용하도록 데이터 배포
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