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Databricks Machine Learning Professional Exam - Topic 12 Question 46 Discussion

Actual exam question for Databricks's Databricks Machine Learning Professional exam
Question #: 46
Topic #: 12
[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 Desirae at Nov 22, 2025, 03:29 AM

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Rhea
11 hours ago
I think option E is a big plus for JS too.
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Verlene
6 days ago
Hmm, I'll have to go with the option that doesn't mention any donuts. Can't be too careful these days.
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Vincent
11 days ago
D) JS is more robust when working with large datasets. That's crucial for real-world machine learning.
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Sylvie
16 days ago
C) None of these reasons. I'm still not convinced JS is better than KS for this use case.
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Joanne
21 days ago
E) JS does not require any manual threshold or cutoff determinations. That's a big plus for production use.
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Janessa
26 days ago
If I recall correctly, the JS distance is indeed more user-friendly since it doesn't require cutoffs, but I’m not sure if that’s the only reason we should choose it over KS.
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Julie
1 month 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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An
1 month ago
I think I saw a practice question about JS being easier because it doesn't need manual thresholds, which might be a key point for this question.
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Rupert
1 month ago
I remember that JS distance is often preferred for its robustness, especially with larger datasets, but I'm not entirely sure if that's the main reason here.
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Albert
2 months ago
Ah, I remember this from class. JS is preferred because it's normalized and doesn't need any manual cutoffs. That makes it a more reliable choice, especially for large-scale applications.
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Christoper
2 months ago
I'm a bit confused on the specifics here. I know JS and KS are both used for feature drift, but I'm not sure about the advantages of one over the other. Guess I'll have to do some more research.
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Venita
2 months ago
Alright, I've got this. JS is more robust than KS when dealing with big data, and it doesn't require manual thresholds. That's gotta be the answer.
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Alease
2 months ago
D) JS is more robust when working with large datasets. That's the key advantage I've heard about.
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Chery
2 months ago
JS is definitely more robust with large datasets!
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Novella
3 months ago
Wait, JS isn't normalized? That sounds odd.
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Deonna
3 months ago
A) All of these reasons. The JS distance just seems more versatile overall.
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Reita
3 months ago
Okay, let me see. I know JS is a good option for large datasets, but I'm not sure about the other reasons. I'll have to review my notes on feature drift detection.
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Marget
3 months ago
Hmm, this seems like a tricky one. I'll need to think through the differences between JS and KS to figure out the best approach.
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Cory
2 months ago
I think JS is better for large datasets.
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