Databricks Exam Databricks-Machine-Learning-Professional Topic 2 Question 39 Discussion

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

Question #: 39
Topic #: 2

[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 Vallie at May 05, 2025, 06:16 PM

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Donte

18 days ago

That's a good point, Jose. It could also be a reason for using JS over KS.

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Jose

25 days ago

But what about E) JS does not require any manual threshold or cutoff determinations?

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Tu

1 months ago

I agree with Donte, JS is definitely better for large datasets.

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Donte

1 months ago

I think the answer is D) JS is more robust when working with large datasets.

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Cherelle

1 months ago

D) JS is more robust when working with large datasets - that's the key reason in my opinion. KS can be sensitive to outliers, but JS handles that better.

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Lashawnda

10 days ago

E) JS does not require any manual threshold or cutoff determinations

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Virgie

16 days ago

D) JS is more robust when working with large datasets

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Ilda

25 days ago

A) All of these reasons

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