Exam AI-100 topic 5 question 34 discussion

Actual exam question from Microsoft's AI-100

Question #: 34
Topic #: 5

You are developing an app that consumes data from several Azure IoT Edge devices.
You need to implement a storage solution for the app. Your solution must allow data to be queried in real-time as it streams into the solution. You need to ensure that your solution provides the least amount of latency for loading data.
You want the data files to persist on the devices for at least 14 days.
What storage solution should you implement?

A. Azure Data Lake Analytics
B. Azure Data Factory Edge
C. Azure HDInsight Hadoop cluster
D. Azure SQL database with In-Memory OLTP

Show Suggested Answer

Suggested Answer: C 🗳️
You can use HDInsight to process streaming data that's received in real time from a variety of devices.
Internet of Things (IoT)
You can use HDInsight to build applications that extract critical insights from data. You can also use Azure Machine Learning on top of that to predict future trends for your business.
By combining enterprise-scale R analytics software with the power of Apache Hadoop and Apache Spark, Microsoft R Server for HDInsight gives you the scale and performance you need. Multi-threaded math libraries and transparent parallelization in R Server handle up to 1000x more data and up to 50x faster speeds than open-source R, which helps you to train more accurate models for better predictions.
Reference:
https://docs.microsoft.com/en-us/azure/hdinsight/hadoop/apache-hadoop-introduction

by Messatsu at July 22, 2021, 2:56 p.m.

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rveney

2 years ago

B. Azure Data Factory Edge To implement a storage solution that allows real-time querying of data with low latency and ensures data persistence on the devices for at least 14 days, you should use Azure Data Factory Edge. Azure Data Factory Edge is designed to collect, transform, and analyze data from edge devices. It provides a scalable and reliable solution for ingesting, processing, and storing data at the edge. With Azure Data Factory Edge, you can configure data flows to ingest data from your Azure IoT Edge devices and store it in the desired storage format (e.g., Azure Blob storage, Azure Data Lake Storage). By using Azure Data Factory Edge, you can set up data pipelines to stream and process the data in real-time, allowing you to query the data as it streams into the solution with minimal latency. Additionally, you can define retention policies to ensure that the data files persist on the devices for at least 14 days.

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YipingRuan

3 years, 11 months ago

What is "You want the data files to persist on the devices for at least 14 days." for?

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Messatsu

3 years, 11 months ago

I would go for D : "In-Memory OLTP increases number of transactions per second and reduces latency for transaction processing. Scenarios that benefit from In-Memory OLTP are: high-throughput transaction processing such as trading and gaming, data ingestion from events or IoT devices, caching, data load, and temporary table and table variable scenarios."

upvoted 3 times

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