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NEW QUESTION: 1
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
You have an initial dataset that contains the crime data from major cities.
You plan to build training models from the training data. You plan to automate the process of adding more data to the training models and to constantly tune the models by using the additional data, including data that is collected in near real-time. The system will be used to analyze event data gathered from many different sources, such as Internet of Things (IoT) devices, live video surveillance, and traffic activities, and to generate predictions of an increased crime risk at a particular time and place.
You have an incoming data stream from Twitter and an incoming data stream from
Facebook, which are event-based only, rather than time-based. You also have a time interval stream every 10 seconds.
The data is in a key/value pair format. The value field represents a number that defines how many times a hashtag occurs within a Facebook post, or how many times a Tweet that contains a specific hashtag is retweeted.
You must use the appropriate data storage, stream analytics techniques, and Azure
HDInsight cluster types for the various tasks associated to the processing pipeline.
You are designing the real-time portion of the input stream processing. The input will be a continuous stream of data and each record will be processed one at a time. The data will come from an Apache Kafka producer.
You need to identify which HDInsight cluster to use for the final processing of the input data. This will be used to generate continuous statistics and real-time analytics. The latency to process each record must be less than one millisecond and tasks must be performed in parallel.
Which type of cluster should you identify?
A. Apache Hadoop
B. Apache Storm
C. Apache Spark
D. Apache HBase
Answer: B
Explanation:
References: https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-storm-overview

NEW QUESTION: 2
Which statement describes the function of the Work Assignment Snap-in?
A. It Is a normalized model for all resources and interactions that provides states for resources and interactions.
B. It Is on agent selection component based on attribute matching across all channels.
C. It Is an enterprise workflow model to orchestrate the omnichannel interaction flow.
D. It Is an engine for tracking and maintaining the end-to-end context of omnichannel Interactions.
Answer: A

NEW QUESTION: 3
귀하의 웹 사이트는 주문형 교육 비디오를 직원에게 제공하고 있습니다. 동영상은 고해상도 MP4 형식으로 매달 업로드됩니다. 직원은 전 세계적으로, 종종 이동 중이며 비디오를 시청하기 위해 HTTP 라이브 스트리밍 (HLS) 프로토콜이 필요한 회사 제공 태블릿을 사용하여 전 세계에 배포됩니다. 귀사에는 비디오 트랜스 코딩 전문 지식이 없으며 필요한 경우 컨설턴트 비용을 지불해야 할 수도 있습니다.
고 가용성 및 비디오 품질을 떨어 뜨리지 않으면 서 가장 비용 효율적인 아키텍처를 어떻게 구현합니까?
A. 원본 고해상도 MP4 비디오를 HLS로 트랜스 코딩하는 Elastic Transcoder.
비디오를 호스팅하기위한 EBS 볼륨 및 며칠 후에 원본 파일을 증분 백업하기 위한 EBS 스냅 샷.
EC2에서 HLS 코드 변환 된 비디오를 제공하는 CloudFront
B. 원본 고해상도 MP4 비디오를 HLS로 트랜스 코딩하는 Elastic Transcoder.
S3는 Lifecycle Management로 비디오를 호스팅하여 며칠 후에 원본 파일을 Glacier에 보관합니다.
CloudFront에서 S3의 HLS 코드 변환 비디오를 제공합니다.
C. SQS를 사용하여 작업을 배포하고 Auto Scaling을 사용하여 대기열 길이에 따라 노드 수를 조정하는 EC2에서 실행되는 비디오 트랜스 코딩 파이프 라인
S3는 Lifecycle Management로 비디오를 호스팅하여 며칠 후에 모든 파일을 Glacier에 보관합니다.
CloudFront에서 Glacier의 HLS 코드 변환 비디오를 제공합니다.
D. SQS를 사용하여 작업을 배포하고 Auto Scaling을 사용하여 EC2에서 실행되는 비디오 트랜스 코딩 파이프 라인은 대기열 길이에 따라 노드 수를 조정합니다.
비디오를 호스팅하기위한 EBS 볼륨 및 며칠 후에 원본 파일을 증분 백업하기 위한 EBS 스냅 샷.
EC2에서 HLS 코드 변환 된 비디오를 제공하는 CloudFront
Answer: B