Databricks Machine Learning Professional Question 238
Select 3You have deployed a machine learning model for predicting customer churn in production. Recently, you noticed a significant drop in the model's accuracy. Upon investigation, you suspect data drift. Which of the following steps should you take to confirm and monitor this issue?
- A
Perform a statistical test to compare the distribution of features in the training data versus the production data.
- B
Examine the model's feature importance to identify the most influential features in the production data.
- C
Implement a data drift monitoring system to continuously track changes in the input data distribution.
- D
Manually retrain the model with the latest production data to address the suspected drift without further analysis.
- E
Check for concept drift by analyzing whether the relationship between input features and the target variable has changed.
Show answer and explanation
Correct answers: A, C, E
Explanation
To effectively address the drop in model performance, you need to confirm whether data drift or concept drift has occurred. First, statistical tests can detect shifts in feature distributions. Second, a data drift monitoring system ensures ongoing observation of production data changes. Finally, analyzing concept drift helps determine if the model's assumptions about feature-target relationships are still valid. These steps provide a systematic approach to diagnosing and addressing drift issues.
- A. Correct.
Performing a statistical test to compare the feature distributions between training and production data is a standard approach to detect data drift. This helps confirm whether there is a significant change in the input data.
- B. Incorrect.
While examining feature importance is useful for understanding the model, it does not directly confirm data drift or monitor it, as feature importance changes might not be directly linked to drift.
- C. Correct.
Implementing a data drift monitoring system is a proactive step to continuously track and alert on changes in input data distribution over time, ensuring early detection of drift.
- D. Incorrect.
Manually retraining the model without confirming the presence of drift or its root cause can lead to wasted effort or suboptimal solutions if the drift is not properly diagnosed.
- E. Correct.
Checking for concept drift ensures that the relationship between input features and the target variable is still reliable. This is essential for understanding whether the model's predictive power has been impacted by changes in the underlying data relationships.