Your question is Handling Noisy Sensor Data. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How do you handle noisy sensor data in a production environment?
Explain a practical machine-learning pipeline for identifying invalid readings, preserving genuine high-frequency signals, training a downstream classifier or regressor, and monitoring quality after deployment. Address missing values, outliers, sensor dropouts, calibration changes, temporal dependencies, and the risk of training-serving skew. Include implementation details and explain how you would evaluate both data-quality handling and downstream model performance.
