Why is model monitoring necessary after model deployment?

Correct answer: To detect changes in data, performance, and errors during real-world use

After deployment, a model works on new, real-world data. Over time, data patterns, user behaviour, or model performance may change, which can increase errors or reduce accuracy. Monitoring helps identify such problems in time so that the model can be improved or updated when needed. Unlike option C, evaluating a model only on training data does not show how it performs in real-world use.