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NEW QUESTION: 1
Following is/are the example/s of customer information related performance indicators
A. Items per bill
B. None of them
C. Average Price
D. Both a & b
Answer: D

NEW QUESTION: 2
한 회사에는 각각 크기가 약 20 바이트 (예보 당 20 기가 바이트) 인 10 억 개의 고유 위치의 출력 해상도로 15 분마다 업데이트 되는 일기 예보를 생성하는 애플리케이션이 있습니다. 매시간 예측 데이터는 전 세계적으로 약 5 백만 번 (초당 1,400 개 요청) 액세스되며 기상 이벤트 중에는 최대 10 배 더 많이 액세스됩니다. 업데이트 할 때마다 예측 데이터를 덮어 씁니다. 현재 일기 예보 애플리케이션의 사용자는 쿼리에 대한 응답이 각 요청에 대해 2 초 이내에 반환 될 것으로 기대합니다.
요구되는 요청 속도와 응답 시간을 충족하는 디자인은 무엇입니까?
A. Amazon EFS 볼륨에 예측 위치를 저장합니다. Amazon EFS 볼륨을 탑재 한 Amazon EC2 인스턴스의 Auto Scaling 집합의 Elastic Load Balancing 그룹을 대상으로하는 Amazon CloudFront 배포를 생성합니다. CloudFront 배포에서 15 분 동안 설정된 캐시 제어 제한 시간을 설정합니다.
B. 예측 위치를 Amazon S3에 개별 객체로 저장합니다. Auto Scaling EC2 인스턴스 집합의 Elastic Load Balancing 그룹을 대상으로하는 Amazon CloudFront 배포를 생성하여 S3 객체의 오리진을 쿼리합니다. CloudFront 배포에서 캐시 제어 제한 시간을 15 분으로 설정합니다.
C. Amazon ES 클러스터에 예측 위치를 저장합니다. 오리진으로 쿼리에 응답하는 AWS Lambda 함수와 함께 API Gateway 엔드 포인트를 대상으로하는 Amazon CloudFront 배포를 사용합니다. 15 분 동안 엣지 위치에서 로컬로 데이터를 캐시하는 Amazon Lambda @ Edge 함수를 생성합니다.
D. Amazon ES 클러스터에 예측 위치를 저장합니다. Amazon API Gateway 엔드 포인트를 대상으로하는 Amazon CloudFront 배포를 오리진으로 쿼리에 응답하는 AWS Lambda 함수와 함께 사용합니다. 캐시 제어 제한 시간을 15 분으로 설정하여 API Gateway 단계에서 API 캐싱을 활성화 합니다.
Answer: D
Explanation:
https://aws.amazon.com/blogs/networking-and-content-delivery/lambdaedge-design-best-practices/

NEW QUESTION: 3
A refinement to the basic Waterfall Model that states that software
should be developed in increments of functional capability is called:
A. Functional refinement
B. Functional development
C. Incremental refinement
D. Incremental development
Answer: D
Explanation:
The advantages of incremental development include the ease of testing increments of functional capability and the opportunity to incorporate user experience into a successively refined product. The other answers are distracters.