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NEW QUESTION: 1
Select and Place:

Answer:
Explanation:

NEW QUESTION: 2
한 회사에서 다중 계층 애플리케이션을 온 프레미스에서 AWS 클라우드로 이동하여 애플리케이션의 성능을 개선하려고합니다. 애플리케이션은 RESTful 서비스를 통해 서로 통신하는 애플리케이션 계층으로 구성됩니다. 한 계층이 오버로드되면 트랜잭션이 삭제됩니다. 솔루션 설계자는 이러한 문제를 해결하고 애플리케이션을 현대화 하는 솔루션을 설계해야 합니다.
이러한 요구 사항을 충족하는 솔루션은 무엇이며 운영상 가장 효율적입니까?
A. Amazon Simple Queue Service (Amazon SQS)를 사용하여 Auto Scaling 그룹의 Amazon EC2에서 실행되는 애플리케이션 서버 간의 메시징 처리 Amazon CloudWatch를 사용하여 SQS 대기열 길이를 모니터링하고 통신 실패가 감지되면 확장
B. Amazon API Gateway를 사용하고 애플리케이션 계층으로 AWS Lambda 기능에 직접 트랜잭션을 사용합니다. Amazon Simple Queue Service (Amazon SQS)를 애플리케이션 서비스 간의 통신 계층으로 사용합니다.
C. Amazon CloudWatch 지표를 사용하여 애플리케이션 성능 기록을 분석하여 성능 장애 중 서버의 최대 사용률을 확인합니다. 애플리케이션 서버의 Amazon EC2 인스턴스 크기를 늘려 최대 요구 사항을 충족합니다.
D. Amazon Simple Notification Service (Amazon SNS)를 사용하여 Auto Scaling 그룹의 Amazon EC2에서 실행되는 애플리케이션 서버 간의 메시징 처리 Amazon CloudWatch를 사용하여 SNS 대기열 길이를 모니터링하고 필요에 따라 확장 및 축소
Answer: A

NEW QUESTION: 3
MapReduce is well-suited for all of the following applications EXCEPT? (Choose one):
A. Analysis of large amounts of Web logs (queries, clicks, etc.).
B. Text mining on a large collections of unstructured documents.
C. Online transaction processing (OLTP) for an e-commerce Website.
D. Graph mining on a large social network (e.g., Facebook friends network).
Answer: C
Explanation:
Hadoop Map/Reduce is designed for batch-oriented work load.
MapReduce is well suited for data warehousing (OLAP), but not for OLTP.