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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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Clare
13 hours ago
I feel like all reasons matter, but D stands out.
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Omer
6 days ago
JS doesn’t need manual thresholds, that's a plus!
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Fabiola
11 days ago
But isn't normalization important?
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Eve
16 days ago
Agreed, it handles data variability well.
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Lynna
21 days ago
I think JS is better for large datasets.
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Josefa
26 days ago
A) All of these reasons sounds too broad to me.
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Jettie
1 month ago
I disagree, KS tests have their own advantages.
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Simona
1 month ago
Wait, JS isn't normalized? That seems odd.
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Annelle
1 month ago
I think option E is a big plus for JS too.
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Lezlie
2 months ago
JS is definitely more robust with large datasets!
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Ruby
2 months ago
All of these reasons make sense to me!
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Lisandra
2 months ago
I agree, no manual thresholds is a big plus for JS!
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Lashawn
2 months ago
Wait, JS isn't normalized? That seems odd.
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Pamela
2 months ago
I thought KS was better for numeric data though.
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Judy
2 months ago
JS is definitely more robust with large datasets!
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Casandra
3 months 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
3 months 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
3 months 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
4 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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