AI Engineering Degree Practice Exam 2025 - Free AI Engineering Practice Questions and Study Guide

Anomalies in data are important indicators of significant deviations from typical behavior. This means that when data points exhibit unusual patterns or values that diverge notably from a defined norm, it often suggests underlying issues such as errors in data collection, processing mistakes, fraudulent activity, or a genuine change in the system being studied. The identification of these anomalies is crucial in various fields, including finance for fraud detection, manufacturing for quality control, and healthcare for unusual patient behavior, as they can lead to insights that prompt corrective actions or further investigations.

Normal behavior, common patterns, and low variance do not accurately capture the essence of what anomalies represent; they are established descriptions of typically expected data characteristics rather than indicators of unusual or unexpected occurrences. Therefore, recognizing and analyzing anomalies is essential for any robust data analysis or AI model to improve understanding and decision-making.