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Databricks Machine Learning Professional Exam - Topic 10 Question 54 Discussion

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

Question #: 54
Topic #: 10

[All Databricks Machine Learning Professional Questions]

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 Marion at May 11, 2026, 12:50 PM

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Judy

3 hours ago

JS is definitely more robust with large datasets!

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Casandra

5 days ago

I believe the answer might be A, since it seems like JS has multiple advantages over KS, but I’m a bit uncertain about the specifics.

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Virgina

10 days ago

I feel like the normalization aspect of JS versus KS was discussed in class, but I can't remember if JS is actually normalized or not.

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Rasheeda

16 days ago

I think I saw a practice question that mentioned JS being more robust with larger datasets, but I can't recall the details.

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Christiane

2 months ago

I remember that JS distance is often preferred because it doesn't require manual thresholds, but I'm not sure if that's the only reason.

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