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Download DemoVendor: Microsoft
Certifications: Role-based
Exam Code: AI-100
Exam Name: Designing and Implementing an Azure AI Solution
Updated: Oct 31, 2023
Q&As: 231
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You design an AI solution that uses an Azure Stream Analytics job to process data from an Azure IoT hub. The IoT hub receives time series data from thousands of IoT devices at a factory.
The job outputs millions of messages per second. Different applications consume the messages as they are available. The messages must be purged.
You need to choose an output type for the job.
What is the best output type to achieve the goal? More than one answer choice may achieve the goal.
A. Azure Event Hubs
B. Azure SQL Database
C. Azure Blob storage
D. Azure Cosmos DB
Correct Answer: D
Stream Analytics can target Azure Cosmos DB for JSON output, enabling data archiving and low-latency queries on unstructured JSON data.
References: https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-documentdb-output
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:
1.
Alert teachers if a student facial expression indicates the student is angry or scared.
2.
Identify each student in the classrooms for attendance purposes.
3.
Allow the teachers to log voice conversations as text. Which Cognitive Services should you recommend?
A. Face API and Text Analytics
B. Computer Vision and Text Analytics
C. QnA Maker and Computer Vision
D. Speech to Text and Face API
Correct Answer: D
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.
Face detection: Detect one or more human faces in an image and get back face rectangles for where in the image the faces are, along with face attributes which contain machine learning-based predictions of facial features. The face attribute
features available are: Age, Emotion, Gender, Pose, Smile, and Facial Hair along with 27 landmarks for each face in the image.
References:
https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/speech-to-text https://azure.microsoft.com/en-us/services/cognitive-services/face/
You are designing an AI system for your company. Your system will consume several Apache Kafka data streams.
You want your system to be able to process the data streams at scale and in real-time.
Which of the following actions should you take?
A. Make use of Azure HDInsight with Apache HBase
B. Make use of Azure HDInsight with Apache Spark
C. Make use of Azure HDInsight with Apache Storm
D. Make use of Azure HDInsight with Microsoft Machine Learning Server
Correct Answer: C
Azure HDInsight is a cloud-based big data analytics service that allows you to process and analyze large volumes of data. It supports various open-source big data technologies, including Apache Kafka, Apache HBase, Apache Spark, and
Apache Storm.
Apache Storm is a distributed real-time processing system designed for processing high-velocity streaming data. It provides a scalable and fault-tolerant platform for processing data streams in real-time. By using Azure HDInsight with Apache
Storm, you can effectively process and analyze your Apache Kafka data streams at scale and in real-time.
Reference:
https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-streaming-at-scale-overview https://docs.microsoft.com/en-us/azure/hdinsight/storm/apache-storm-overview
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