Databricks Machine Learning Professional Question 240
Single answerYou are monitoring a deployed machine learning model that predicts house prices. One of the numeric features, 'Square Footage', shows a significant change in its mean and variance compared to the training dataset. Which of the following best describes how summary statistic monitoring helps in identifying this numeric feature drift?
- A
Summary statistic monitoring tracks changes in statistical properties like mean, variance, and standard deviation of numeric features over time.
- B
Summary statistic monitoring compares the distribution of numeric features to an external dataset to detect anomalies.
- C
Summary statistic monitoring ensures the numeric features of the deployed model remain identical to those in the training dataset.
- D
Summary statistic monitoring uses advanced machine learning algorithms to predict feature drift before it happens.
Show answer and explanation
Correct answer: A
Explanation
Summary statistic monitoring is a simple yet effective solution for detecting numeric feature drift by comparing statistical properties (e.g., mean, variance, standard deviation) of features in the training and deployed datasets over time. It helps identify significant shifts in the data, such as the observed change in 'Square Footage', making it an important tool for model monitoring.
- A. Correct.
Correct: Summary statistic monitoring is a simple and effective solution to detect numeric feature drift by tracking changes in statistical properties such as mean, variance, and standard deviation over time.
- B. Incorrect.
Incorrect: While summary statistic monitoring may detect drift, it does not rely on external datasets for anomaly detection. It uses statistical properties of the feature itself.
- C. Incorrect.
Incorrect: Numeric features in deployment are not expected to remain identical to those in the training dataset due to natural variations. Summary statistic monitoring detects significant shifts, not ensures identical values.
- D. Incorrect.
Incorrect: Summary statistic monitoring does not involve predictive algorithms. It is a straightforward statistical method to monitor feature drift.