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NEW QUESTION: 1
Which option is the embedded component that enables endpoints and collaboration applications to support medianet functionality?
A. SNMP
B. Medianet Agent
C. Open API
D. SDN
E. XMPP
F. Media Services Interface
Answer: F
Explanation:
Explanation/Reference:
Explanation:
Media Services Interface (MSI) is a software component that is embedded in video endpoints and collaboration applications. The MSI software developer's kit provides a set of APIs enabling applications to use medianet network services such as Autoconfiguration and Media Monitoring.
MSI ties the network to user devices and applications to enable for a true end-to-end architecture called Cisco Medianet.
MSI-embedded endpoints and applications are more tightly integrated with the network to help the organization:
* Help reduce the costs of deploying and managing video endpoints
* Quickly troubleshoot issues and help cut operational costs
* Detect network issues while they occur for immediate remediation and improved user experience
* Enable the network to accurately apply network services to enhance user experience.
Reference: http://www.cisco.com/c/en/us/solutions/collateral/enterprise/enterprise-medianet/qa_C67-
511731.html
NEW QUESTION: 2
A drug ending in the suffix (pril) is considered a ______.
A. Antifungal
B. H2 agonist
C. ACE inhibitor
D. Beta agonist
Answer: C
NEW QUESTION: 3
What is the provisioning timescale for each SaaS offering?
A. ten days from date of order being received by HP SaaS
B. as specified in the corresponding solution data sheet
C. two hours from time of order being received by HP SaaS
D. one day from date of order being received by HP SaaS
Answer: A
NEW QUESTION: 4
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 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.
C. 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.
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: B
Explanation:
Explanation
https://github.com/aws-samples/lambda-refarch-imagerecognition