Databricks Machine Learning Professional Question 251
Select 2You are monitoring a deployed machine learning model, and you notice that the distribution of a key numeric feature in the input data has shifted significantly over time. Instead of relying solely on summary statistics (e.g., mean and standard deviation), you decide to implement statistical tests to detect numeric feature drift. Why are statistical tests a more robust solution in this scenario?
- A
Statistical tests account for entire feature distributions, not just summary metrics like mean or variance.
- B
Statistical tests can identify specific subpopulations within the data that are contributing to drift.
- C
Statistical tests are computationally faster than calculating summary statistics like mean and variance.
- D
Statistical tests provide probabilistic evidence (e.g., p-values) to quantify the likelihood of feature drift.
Show answer and explanation
Correct answers: A, D
Explanation
Statistical tests are a more robust solution for detecting numeric feature drift because they analyze the full distribution of a feature rather than relying on a small set of summary metrics like mean or variance. This makes them capable of identifying subtle or complex changes in feature behavior. Moreover, the use of probabilistic evidence, such as p-values, allows for a quantifiable and interpretable assessment of whether the observed drift is statistically significant.
- A. Correct.
Correct. Statistical tests analyze the entire distribution of the numeric feature, which allows them to capture changes in patterns that summary statistics like mean or variance might miss.
- B. Incorrect.
Incorrect. While statistical tests are useful for detecting overall distribution shifts, they do not inherently identify specific subpopulations causing the drift unless additional analysis is performed.
- C. Incorrect.
Incorrect. Statistical tests often involve more computational complexity than summary statistics like mean and variance, as they compare entire distributions.
- D. Correct.
Correct. Statistical tests provide a rigorous, probabilistic measure (e.g., p-values) to assess the likelihood that the observed drift is due to random chance, making them more reliable for monitoring.