A manufacturer streams vibration readings from its pumps. Nearly all readings are normal, and the team wants to flag rare readings that deviate sharply from typical behavior. It has no labeled examples of failures. Which kind of task is this?
Show answer and explanation
Answer: B
Flagging rare readings that differ sharply from normal behavior is anomaly detection. With no failure labels, the approach is usually unsupervised, such as learning what typical readings look like and scoring deviations from that baseline. Classifying pump models requires labeled categories, dimensionality reduction compresses features rather than detecting events, and generating synthetic signals creates new data instead of identifying unusual readings. The absence of labeled failures is a strong clue here.
Why the other options are wrong
- A. Classifying pump models assumes labeled categories of equipment, and the goal described is spotting unusual readings rather than assigning a model type.
- C. Dimensionality reduction compresses features for storage or visualization, but it does not by itself identify which individual readings are unusual.
- D. Generating synthetic signals produces new data samples, which is a generative task and does not flag the rare real readings the manufacturer needs to catch.
Exam tip. Rare, unexpected events with few or no labels usually point to anomaly detection.