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Databricks Machine Learning Professional Exam - Topic 10 Question 54 Discussion

Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?
D) JS is more robust when working with large datasets
A) All of these reasons
B) JS is not normalized or smoothed
C) None of these reasons
E) JS does not require any manual threshold or cutoff determinations

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?

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Suggested Answer: D

by Marion at May 11, 2026, 12:50 PM

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Lezlie
1 day ago
JS is definitely more robust with large datasets!
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Ruby
6 days ago
All of these reasons make sense to me!
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Lisandra
11 days ago
I agree, no manual thresholds is a big plus for JS!
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Lashawn
17 days ago
Wait, JS isn't normalized? That seems odd.
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Pamela
22 days ago
I thought KS was better for numeric data though.
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Judy
27 days ago
JS is definitely more robust with large datasets!
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Casandra
1 month 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
1 month 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
1 month 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
3 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.
upvoted 0 times
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