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NEW QUESTION: 1
제조 회사는 고객 사이트에서 실행되는 컴퓨터에서 데이터를 캡처합니다. 현재 수천 대의 컴퓨터가 5 분마다 데이터를 전송하므로 가까운 시일 내에 수십만 대의 컴퓨터로 확장 될 것으로 예상됩니다. 필요한 경우 나중에 분석 할 의도로 데이터가 기록됩니다.
이 스트리밍 데이터를 대규모로 저장하는 SIMPLEST 방법은 무엇입니까?
A. ELB 뒤에 Amazon EC2 서버 팜을 생성하여 Amazon EBS Cold FIDD 볼륨에 데이터를 저장합니다.
B. Amazon Kinesis Firehouse 전송 스트림을 생성하여 Amazon S3에 데이터를 저장합니다.
C. Amazon SQS 대기열을 생성하고 머신이 대기열에 쓰도록 합니다.
D. ELB 뒤에 Amazon EC2 서버의 Auto Scaling 그룹을 만들어 데이터를 Amazon RDS에 씁니다.
Answer: B
Explanation:
Explanation
What Is Amazon Kinesis Data Firehose?
Amazon Kinesis Data Firehose is a fully managed service for delivering real-time streaming data to destinations such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon Elasticsearch Service (Amazon ES), and Splunk. Kinesis Data Firehose is part of the Kinesis streaming data platform, along with Kinesis Data Streams, Kinesis Video Streams, and Amazon Kinesis Data Analytics. With Kinesis Data Firehose, you don't need to write applications or manage resources. You configure your data producers to send data to Kinesis Data Firehose, and it automatically delivers the data to the destination that you specified. You can also configure Kinesis Data Firehose to transform your data before delivering it
NEW QUESTION: 2
When adding an agentless server in the Device Management Console (DMS), which information must be specified? (select 2)
A. IP address or DNS fully-qualified name of the server
B. WBEM login credentials
C. SNMP community string
D. Onboard Administrator login credentials
E. IP address or DNS fully-qualified name of the iLO
Answer: C,E
NEW QUESTION: 3
A Solutions Architect is designing a photo application on AWS. Every time a user uploads a photo to Amazon S3, the Architect must insert a new item to a DynamoDB table.
Which AWS-managed service is the BEST fit to insert the item?
A. Lambda@Edge
B. Amazon EC2 instances
C. AWS Lambda
D. Amazon API Gateway
Answer: C
Explanation:
Explanation/Reference:
Reference https://aws.amazon.com/blogs/machine-learning/build-your-own-face-recognition-service-using- amazon-rekognition/
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