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NEW QUESTION: 1
What process uses test data as part of a comprehensive test of program controls in a continuous online manner?
A. Test data/deck
B. Parallel simulation
C. Base-case system evaluation
D. Integrated test facility (ITF)
Answer: C
Explanation:
A base-case system evaluation uses test data sets developed as part of comprehensive testing programs, it is used to verify correct systems operations before acceptance, as well as periodic validation. Test data/deck simulates transactions through real programs. An ITF creates fictitious files in the database with test transactions processed simultaneously with live input. Parallel simulation is the production of data processed using computer programs that simulate application program logic.

NEW QUESTION: 2
What VPN feature allows traffic to exit the security appliance through the same interface it entered?
A. NAT traversal
B. hairpinning
C. split tunneling
D. NAT
Answer: B
Explanation:
Explanation
In network computing, hairpinning (or NAT loopback) describes a communication between two hosts behind
the same NAT device using their mapped endpoint. Because not all NAT devices support this communication
configuration, applications must be aware of it.
Hairpinning is where a machine on the LAN is able to access another machine on the LAN via the external IP
address of the LAN/router (with port forwarding set up on the router to direct requests to the appropriate
machine on the LAN).
Source: https://en.wikipedia.org/wiki/Hairpinning

NEW QUESTION: 3
사진 공유 및 출판 회사는 매일 10,000 ~ 150,000 개의 이미지를 받습니다. 회사는 여러 공급 업체 및 서비스에 등록 된 사용자로부터 이미지를 받습니다. 이 회사는 AWS로 이전하고 있으며 Amazon Rekognition을 사용하여 데이터를 추가하여 기존 메타 데이터를 보강하려고 합니다.
다음은 추가 데이터의 예입니다.

클라우드 마이그레이션 프로그램의 일환으로 이 회사는 기존 이미지 데이터를 Amazon S3에 업로드하고 사용자에게 이미지를 Amazon S3에 직접 업로드하도록 지시했습니다.
이러한 요구 사항을 지원하기 위해 솔루션 아키텍트는 어떻게 해야 합니까?
A. S3 이벤트 알림을 기반으로 AWS Lambda를 트리거하여 Amazon Rekognition을 사용하여 추가 메타 데이터를 생성합니다. Amazon RDS MySQL Multi-AZ를 사용하여 메타 데이터 정보를 저장하고 Lambda를 사용하여 인덱스를 생성하십시오. Lambda가 지원하는 검색 기능이있는 웹 프런트 엔드를 사용하십시오.
B. S3 이벤트 알림을 기반으로 Amazon SQS 대기열을 시작합니다. 그런 다음 Amazon SQS가 메타 데이터 정보를 Amazon DynamoDB로 보내도록합니다. Amazon EC2에서 실행되는 애플리케이션은 API를 사용하여 Amazon Rekognition에서 데이터를 추출하고 DynamoDB 및 Amazon ES에 데이터를 추가합니다. 웹 프론트 엔드를 사용하여 Amazon ES가 지원하는 검색 기능을 제공하십시오.
C. S3 이벤트 알림을 기반으로 AWS Lambda를 트리거하여 Amazon Rekognition을 사용하여 추가 메타 데이터를 생성합니다. Amazon DynamoDB를 사용하여 메타 데이터를 저장하고 Amazon ES를 사용하여 인덱스를 생성하십시오. 웹 프론트 엔드를 사용하여 Amazon ES가 지원하는 검색 기능을 제공하십시오.
D. Amazon Kinesis를 사용하여 S3 이벤트를 기반으로 데이터를 스트리밍하십시오. Amazon EC2에서 실행되는 애플리케이션을 사용하여 이미지에서 메타 데이터를 추출하십시오. 그런 다음 Amazon DynamoDB 및 Amazon CloudSearch에 데이터를 저장하고 인덱스를 생성하십시오. CloudSearch가 지원하는 검색 기능이 있는 웹 프런트 엔드를 사용하십시오.
Answer: C
Explanation:
Explanation
https://github.com/aws-samples/lambda-refarch-imagerecognition

NEW QUESTION: 4
Click the Exhibit button.

Referring to the exhibit, which statement is correct?
A. This router is an ABR.
B. This router is connected to 27 different areas.
C. This router is an ASBR.
D. This router originated the LSA.
Answer: D