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QUESTION NO: 1
회사에서 Amazon DynamoDB를 사용하여 제품 카탈로그 (1GB)를 준비하고 있습니다. 제품
항목은 평균 100KB의 데이터로 구성되며 평균 트래픽은 초당 약 250 개의 요청이므로
데이터베이스 관리자는 3,000 개의 RCU의 읽기 용량 처리량을 프로비저닝했습니다.
그러나 일부 제품은 매우 인기가 있으며 제한으로 인해 사용자에게 지연 또는 시간 초과가
발생합니다.
어떤 개선이 이 문제에 대한 장기적인 해결책을 제공합니까?
A. 처리량 프로비저닝을 6.000 RCU로 증가
B. Amazon DynamoDB Accelerator를 사용하여 자주 읽는 항목을 유지
C. 주요 제품 속성만 저장하고 Amazon S3에 세부 정보를 저장하여 Amazon DynamoDB를
확장합니다.
D. 파티션 키를 제품 키 대신 제품 키 및 제품 유형의 해시로 구성하도록 변경하십시오.
Answer: B
QUESTION NO: 2
Solutions Architect는 환경마다 다른 데이터베이스 암호가 필요한 AWS Lambda를 사용하여
솔루션을 설계하고 있습니다.
안전하고 확장 가능한 방식으로이를 달성하기 위해 건축가는 어떻게 해야 합니까?
A. 각 개별 환경에 대한 Lambda 함수를 만듭니다.
B. Amazon DynamoDB를 사용하여 환경 변수를 저장하십시오.
C. 암호화 된 AWS Lambda 환경 변수를 사용하십시오.
D. 변수 분배를위한 전용 Lambda 함수를 구현하십시오.
Answer: C
Explanation
https://docs.amazonaws.cn/en_us/lambda/latest/dg/env_variables.html
QUESTION NO: 3
레거시 빌드 관리 애플리케이션은 아티팩트를 400 서버가 액세스하는 NFS 공유 파일
시스템에 저장합니다.
회사는 인프라를 AWS로 마이그레이션하고 있습니다.
빌드 관리에 어떤 스토리지 서비스를 사용해야 합니까?
A. Amazon S3
B. Amazon EFS
C. Amazon EBS
D. Amazon EC2 Instance Storage
Answer: B
QUESTION NO: 4
게임 애플리케이션은 캐싱에 크게 의존하고 Redis 용 Amazon ElastiCache를 사용합니다.
캐시 노드의 장애로 인해 애플리케이션 성능이 최근 저하되었습니다.
향후 성능 저하를 최소화하기 위해 Solutions Architect가 권장할 사항은 무엇입니까?
A. ElastiCache에서 Amazon RDS로 마이그레이션
B. 캐시 데이터를 저장하도록 자동 백업 구성
C. 자동 장애 조치로 ElastiCache 다중 AZ 구성
D. Auto Scaling을 사용하여 CPU 사용량을 기반으로 캐시 노드를 프로비저닝
Answer: C
Explanation
Enabling ElastiCache Multi-AZ with automatic failover on your Redis cluster (in the API and
CLI, replication group) improves your fault tolerance. This is true particularly in cases where your cluster's read/write primary cluster becomes unreachable or fails for any reason. Multi-
AZ with automatic failover is only supported on Redis clusters that support replication.
QUESTION NO: 5
회사는 대규모 데이터 세트를 분석하기 위해 Amazon S3에서 데이터 레이크 솔루션을
개발하고 있습니다. 이 솔루션은 드문 SQL 쿼리 만 만듭니다. 또한 회사는 인프라 비용을
최소화하고자 합니다.
이러한 요구 사항을 충족하기 위해 어떤 AWS 서비스를 사용해야 합니까?
A. 아마존 아테나
B. 아마존 레드 시프트 스펙트럼
C. PostgreSQL 용 Amazon RDS
D. 아마존 오로라
Answer: A
Explanation
https://docs.aws.amazon.com/whitepapers/latest/building-data-lakes/in-place-querying.html
Athena is a serverless service and does not need any infrastructure to create, manage, or scale data sets. It works directly on top of Amazon S3 data sets. It creates external tables and therefore does not manipulate S3 data sources.
"You would typically use Athena for ad hoc data discovery and SQL querying, and then use
Redshift Spectrum for more complex queries and scenarios where a large number of data lake users want to run concurrent BI and reporting workloads."
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Updated: May 28, 2022