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NEW QUESTION: 1
You are designing an AI solution that will provide feedback to teachers who train students over the Internet.
The students will be in classrooms located in remote areas. The solution will capture video and audio data of the students in the classrooms.
You need to recommend Azure Cognitive Services for the AI solution to meet the following requirements:
Alert teachers if a student seems angry or distracted.
Identify each student in the classrooms for attendance purposes.
Allow the teachers to log the text of conversations between themselves and the students.
Which Cognitive Services should you recommend?
A. Computer Vision, Text Analytics, and Face API
B. Text Analytics, QnA Maker, and Computer Vision
C. Video Indexer, Face API, and Text Analytics
D. Video Indexer, Speech to Text, and Face API
E. Computer Vision, Speech to Text, and Text Analytics
Answer: D
Explanation:
Explanation
Azure Video Indexer is a cloud application built on Azure Media Analytics, Azure Search, Cognitive Services (such as the Face API, Microsoft Translator, the Computer Vision API, and Custom Speech Service). It enables you to extract the insights from your videos using Video Indexer video and audio models.
Face API enables you to search, identify, and match faces in your private repository of up to 1 million people.
The Face API now integrates emotion recognition, returning the confidence across a set of emotions for each face in the image such as anger, contempt, disgust, fear, happiness, neutral, sadness, and surprise. These emotions are understood to be cross-culturally and universally communicated with particular facial expressions.
Speech-to-text from Azure Speech Services, also known as speech-to-text, enables real-time transcription of audio streams into text that your applications, tools, or devices can consume, display, and take action on as command input. This service is powered by the same recognition technology that Microsoft uses for Cortana and Office products, and works seamlessly with the translation and text-to-speech.

NEW QUESTION: 2
모바일 게임 회사는 Amazon EC2 인스턴스에서 응용 프로그램 서버를 실행합니다. 서버는 15 분마다 플레이어로부터 업데이트를 받습니다. 모바일 게임은 마지막 업데이트 이후 게임 진행 상황에 대한 JSON 개체를 생성하고 JSON 개체에 Application Load Balacer를 보냅니다.
모바일 게임이 실행되면서 게임 업데이트가 손실됩니다. 회사는 업데이트를 순서대로 가져 오는 내구성있는 방법을 만들고자 합니다.
솔루션 설계자는 시스템을 분리하기 위해 무엇을 권장해야 합니까?
A. Amazon Kinesis Data Firehouse를 사용하여 데이터를 캡처하고 Amzon S3에 JSON 객체를 저장합니다.
B. Amazon Simple Notification Service (Amazon SNS)를 사용하여 데이터와 EC2 인스턴스를 캡처하여 Application Load Balancer로 전송 된 메시지를 처리합니다.
C. Amazon Kinesis Data 스트림을 사용하여 데이터를 캡처하고 Amazon S3에 JSON 객체를 저장합니다.
D. Amazon Simple Queue Service (Amzon SQS) FIFO 대기열을 사용하여 데이터와 EC2 인스턴스를 캡처하여 대기열의 메시지를 처리합니다.
Answer: D

NEW QUESTION: 3
Which protocol relies on IQNs to identify initiators and targets?
A. FCoE
B. iSOSI
C. SAS
D. FCP
Answer: B