AI-900: How to use Anomaly Detector to monitor production line failures

Learn how to use Anomaly Detector, an Azure AI service, to monitor and detect failures in your automated production line with machine learning. Find out why Anomaly Detector is better than Form Recognizer or Computer Vision for this use case.

Question

Suppose you’re creating a software system that tracks activity in an automated production line and you want it to be able to identify failures. What Al cognitive service should you implement in the development of the system?

A. Form Recognizer
B. Computer Vision
C. Anomaly Detector

Answer

C. Anomaly Detector

Explanation

These types of scenario can be addressed by using anomaly detection – a machine learning based technique that analyzes data over time and identifies unusual changes.

The correct answer is C. Anomaly Detector. Anomaly Detector is an Azure AI service that enables you to monitor and detect abnormalities in your time series data with machine learning. It can help you identify problems such as failures, dips, spikes, or level changes in your data streams that might otherwise be hard to notice. Anomaly Detector can also provide explanations for the detected anomalies, such as the severity, expected value, and root cause analysis.

Form Recognizer and Computer Vision are not suitable for this scenario, because they are designed to analyze and extract information from images, documents, and forms, not time series data. Form Recognizer can help you automate data entry and extraction from forms, invoices, receipts, and other structured or semi-structured documents. Computer Vision can help you understand the content and context of images and videos, such as faces, objects, scenes, text, and emotions.

Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.