Data Drift Detection 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 best describes 'Data Drift' in the context of machine learning model monitoring?

2. 2

   You observe that your model's performance on a loan approval task is declining. Upon inspection, you find that the distribution of 'annual income' in production has shifted significantly higher than in the training set. Which monitoring strategy is most appropriate to detect this?

3. 3

   When monitoring for data drift in high-dimensional categorical features, why might a simple frequency count comparison be insufficient?

4. 4

   Which of the following is a potential consequence of ignoring data drift in a production ML system?

5. 5

   You are designing a drift detection pipeline. If you use Jensen-Shannon (JS) divergence to monitor drift, what is the primary advantage of choosing this metric over Kullback-Leibler (KL) divergence in a production environment?