A machine learning engineer is monitoring categorical input variables for a production machine learning application.

A two-way Chi-squared test is a statistical test that can be used to compare the frequencies of categorical variables across two or more groups. In this case, the engineer can use a two-way Chi-squared test to compare the frequencies of the categorical input variable values across different time periods (e.g., months, quarters, etc.) and see if there is a significant difference in the proportion of missing values. A significant difference would indicate that the missing values are not randomly distributed and may affect the performance of the machine learning model. References:

Databricks Machine Learning Professional Exam Guide, Section 4: Solution and Data Monitoring, p. 5

Databricks Machine Learning Documentation, Monitoring ML Models, Data Drift Detection, p. 3