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PMLE Practice Question: A team has deployed a model on Vertex AI…
A team has deployed a model on Vertex AI Prediction and wants to monitor for data drift. Which TWO metrics should they use to detect drift in numerical features?
⚠ Common exam trap
Google Cloud often tests the misconception that Pearson correlation or Chi-squared are appropriate for numerical drift, when in fact Pearson measures correlation between two variables and Chi-squared is for categorical data, leading candidates to overlook the correct distribution-comparison metrics like JSD and KS.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Jensen-Shannon divergence (JSD)
Jensen-Shannon divergence (JSD) is a symmetric, bounded (0 to 1) measure of the difference between two probability distributions, making it ideal for detecting drift in numerical features by comparing the training distribution to the serving distribution. It is a smoothed and normalized version of Kullback-Leibler divergence, and Vertex AI Prediction's Model Monitoring natively supports JSD for numerical feature drift detection.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Pearson correlation coefficient
Why it's wrong here
Pearson correlation measures linear relationship between two variables, not distribution drift.
- ✓
Jensen-Shannon divergence (JSD)
Why this is correct
JSD measures similarity between two probability distributions and works for numerical features after binning.
- ✗
Chi-squared statistic
Why it's wrong here
Chi-squared test is for categorical features.
- ✗
Population Stability Index (PSI)
Why it's wrong here
PSI is primarily used for categorical features or binned numerical data, but not as a direct metric for continuous distributions.
- ✓
Kolmogorov-Smirnov (KS) statistic
Why this is correct
KS test is used to compare two distributions and detect shift in numerical features.
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