Learn how IoT devices use anomaly detection to monitor engine temperatures and generate alerts for deviations. Ideal for AI-900 exam preparation.
Table of Contents
Question
You have an Internet of Things (loT) device that monitors engine temperature. The device generates an alert if the engine temperature deviates from expected norms. Which type of Al workload does the device represent?
A. natural language processing (NLP)
B. computer vision
C. anomaly detection
D. knowledge mining
Answer
C. anomaly detection
Explanation
Anomaly detection identifies patterns in data that deviate significantly from the norm, making it ideal for tasks where unusual behaviors or conditions must be flagged. In the scenario described, the IoT device continuously monitors engine temperature and generates alerts when deviations from expected norms occur.
Why other options are incorrect:
A. Natural Language Processing (NLP): NLP focuses on interpreting, understanding, and generating human language, unrelated to temperature monitoring.
B. Computer Vision: This involves interpreting visual data from images or videos, not relevant to detecting temperature anomalies.
D. Knowledge Mining: Involves extracting useful insights from unstructured data, which is unrelated to real-time anomaly detection in temperature data.
Anomaly detection enables IoT systems to maintain safety, efficiency, and reliability by identifying potential issues early.
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