Databricks Machine Learning Associate Exam - Topic 1 Question 32 Discussion

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

Question #: 32
Topic #: 1

[All Databricks Machine Learning Associate Questions]

What is the name of the method that transforms categorical features into a series of binary indicator feature variables?

ALeave-one-out encoding

BTarget encoding

COne-hot encoding

DCategorical

EString indexing

Show Suggested Answer

Suggested Answer: C

The method that transforms categorical features into a series of binary indicator variables is known as one-hot encoding. This technique converts each categorical value into a new binary column, which is essential for models that require numerical input. One-hot encoding is widely used because it helps to handle categorical data without introducing a false ordinal relationship among categories. Reference:

Feature Engineering Techniques (One-Hot Encoding).

by Nelida at Jul 09, 2025, 01:19 AM

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Veronica

4 months ago

Wait, are we sure it's not Target encoding?

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Breana

4 months ago

Agreed, that's the standard method.

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Gaston

4 months ago

It's definitely One-hot encoding!

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Susana

4 months ago

I thought it was Leave-one-out encoding at first.

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Na

4 months ago

One-hot is the way to go for categorical features!

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Leandro

5 months ago

I’m a bit confused; I thought categorical encoding was a broader term that included one-hot encoding, but I can't recall the specifics.

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Kaycee

5 months ago

I feel like there was something about target encoding too, but it seems more related to the target variable than just transforming features.

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Fidelia

5 months ago

I remember practicing a question about encoding techniques, and one-hot encoding was definitely mentioned there.

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Roy

5 months ago

I think the method we're looking for is one-hot encoding, but I'm not completely sure.

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Isadora

5 months ago

One-hot encoding, that's the one! I remember learning about this in my machine learning class. It's a really useful technique for converting categorical features into a format that can be used in models.

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Trinidad

6 months ago

I'm a little confused by this question. There are a few different techniques for dealing with categorical data, like leave-one-out encoding and target encoding. I'm not 100% sure which one is being asked about here.

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Audrie

6 months ago

Okay, I think I've got this. The method that transforms categorical features into binary indicator variables is called one-hot encoding. That's definitely the right answer here.

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Fernanda

6 months ago

Hmm, I'm a bit unsure about this one. I know there are a few different ways to handle categorical features, but I can't quite remember the specific name for the method that creates binary indicator variables. I'll have to think this through carefully.

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Kristal

6 months ago

I'm pretty sure this is asking about one-hot encoding, which is a common technique for transforming categorical features into a format that can be used in machine learning models.

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