Model Performance Monitoring Quiz

Quiz covering Model Monitoring

Questions in this quiz

A preview of the 5 questions covered. Start the quiz above to answer them, check your score, and read the explanations.

1. 1

   Which of the following metrics is most commonly used to detect 'Data Drift' in numerical input features?

2. 2

   What is the primary purpose of implementing model monitoring in a production ML pipeline?

3. 3

   If your model's prediction accuracy drops, but the input data distribution remains identical to the training set, what is the most likely cause?

4. 4

   You notice that a feature's values in production have shifted significantly compared to the training set, but the model's overall accuracy remains stable. What is the best course of action?

5. 5

   When monitoring a model that predicts a continuous target variable, why is 'Mean Absolute Error' (MAE) monitoring preferred over 'Mean Squared Error' (MSE) in systems with outliers?