Struggling with Azure AI anomaly detection? Ace the AI-102 exam! Learn how to use Multivariate Anomaly Detection APIs in Azure AI to monitor IoT sensor data, analyze correlations, and set up alerts. Get the best approach for your manufacturing plant’s monitoring system!
Table of Contents
Question
Your organization, Nutex Inc., is implementing a monitoring system for an Azure AI resource that processes complex data from various Internet of Things (IoT) sensors in a manufacturing plant. The system must detect anomalies across multiple metrics, such as temperature, pressure, and vibration, simultaneously.
To effectively monitor and detect anomalies in your sensor data, which approach should you take using Azure AI services?
A. Utilize Univariate Anomaly Detection APIs in Azure AI to analyze correlations between multiple metrics and detect anomalies, then set up automated alerts.
B. Configure single-variable alerts in Azure Monitor for each sensor metric individually.
C. Utilize Multivariate Anomaly Detection APIs in Azure AI to analyze correlations between multiple metrics and detect anomalies, then set up automated alerts.
D. Deploy Azure Stream Analytics to process the sensor data and use its built-in anomaly detection capabilities.
Answer
C. Utilize Multivariate Anomaly Detection APIs in Azure AI to analyze correlations between multiple metrics and detect anomalies, then set up automated alerts.
Explanation
In the given scenario, you would utilize Multivariate Anomaly Detection APIs in Azure AI to analyze correlations between multiple metrics and detect anomalies, then set up automated alerts. This approach ensures the comprehensive analysis of multiple correlated metrics with automated alerting, making it ideal for sophisticated monitoring requirements. Multivariate Anomaly Detection in Azure AI is specifically designed to handle scenarios where multiple metrics are interdependent. It uses advanced algorithms to analyze the relationships between various metrics and detect anomalies that may not be evident when looking at individual metrics in isolation. This makes it ideal for monitoring complex systems like those in a manufacturing plant, where there are many metrics such as temperature, pressure, and vibration are closely related. Multivariate Anomaly Detection APIs can be used to look at deviations with temperature, pressure, and vibration and evaluate the health of the plant.
You would not configure single-variable alerts in Azure Monitor for each sensor metric individually in the given scenario. Azure Monitor allows you to set up alerts based on specific thresholds for individual metrics. This approach is useful for simple monitoring tasks. However, it falls short when dealing with complex scenarios where multiple metrics are interdependent.
You would not deploy Azure Stream Analytics to process the sensor data and use its built-in anomaly detection capabilities in the given scenario. Azure Stream Analytics is best suited for real-time data processing and can handle large volumes of data. However, it is designed for stream processing. While it can process individual metrics and apply simple anomaly detection, it lacks the sophistication needed to simultaneously analyze complex, interrelated metrics.
You would not utilize Univariate Anomaly Detection in Azure AI to analyze correlations between multiple metrics and detect anomalies, then set up automated alerts in the given scenario. Univariate Anomaly Detection involves identifying anomalies in data sets that contain a single variable. It focuses on detecting values that deviate significantly from the expected range for that one variable. The main goal is to spot unusual patterns or outliers within a single variable, which could indicate issues such as errors, fraud, or other significant events.
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