Databricks Machine Learning Professional Exam - Topic 8 Question 52 Discussion

Actual exam question for Databricks's Databricks Machine Learning Professional exam

Question #: 52
Topic #: 8

Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?

AAll of these reasons

BJS is not normalized or smoothed

CNone of these reasons

DJS is more robust when working with large datasets

EJS does not require any manual threshold or cutoff determinations

Show Suggested Answer

Suggested Answer: D

by Golda at Apr 07, 2026, 01:14 PM

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4 days ago

I’m a bit confused about the normalization aspect; I thought both methods had their own ways of handling that.

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9 days ago

I practiced a similar question, and I feel like JS doesn't need manual thresholds, which is a big plus compared to KS.

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14 days ago

I think I remember that JS distance is more robust with large datasets, but I'm not entirely sure if that's the main reason.

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