CompTIA DY0-001 Exam - Topic 3 Question 2 Discussion

Actual exam question for CompTIA's DY0-001 exam

Question #: 2
Topic #: 3

A data scientist is analyzing a data set with categorical features and would like to make those features more useful when building a model. Which of the following data transformation techniques should the data scientist use? (Choose two.)

ANormalization

BOne-hot encoding

CLinearization

DLabel encoding

EScaling

FPivoting

Show Suggested Answer

Suggested Answer: B

One-hot encoding creates binary indicator columns for each category, allowing models to treat nominal categories without implying any order.

Label encoding maps categories to integer labels, which can be useful for tree-based models or when you need a single numeric column (though you must ensure the algorithm can handle treated ordinality appropriately).

by Rosalind at Jun 13, 2025, 02:02 AM

Limited Time Offer

25%

Off

Get Premium DY0-001 Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel

Rolande

2 months ago

Pivoting could help in some cases, but not a primary method here.

upvoted 0 times

...

Valentin

2 months ago

Wait, is linearization even a thing for categorical data? Sounds off.

upvoted 0 times

...

Tony

2 months ago

One-hot encoding is definitely a go-to for categorical features!

upvoted 0 times

...

Tish

3 months ago

I think label encoding can be useful too, especially for ordinal data.

upvoted 0 times

...

Tamesha

3 months ago

Normalization? Not really needed for categorical features.

upvoted 0 times

...

Ardella

3 months ago

I vaguely recall something about pivoting, but I don't think that's what we need here. One-hot encoding definitely sounds right!

upvoted 0 times

...

Deeanna

3 months ago

I feel like normalization and scaling are more for numerical data, so I’m leaning towards one-hot and label encoding.

upvoted 0 times

...

Gracia

4 months ago

I'm not entirely sure, but I think label encoding might also be useful. It can convert categories to numbers, right?

upvoted 0 times

...

Arlette

4 months ago

I remember we talked about one-hot encoding in class; it seems like a good choice for categorical features.

upvoted 0 times

...

Dudley

4 months ago

One-hot encoding is definitely one of the techniques I would use. For the other, I'm torn between label encoding and normalization. I'll have to think about which one would be more appropriate here.

upvoted 0 times

...

Annamaria

4 months ago

I'm confident that one-hot encoding is the way to go for this. It creates new binary columns for each category, which should help the model work with the categorical data. Not sure about the other option though.

upvoted 0 times

...

Eveline

4 months ago

Okay, let me think this through. One-hot encoding converts categorical variables into a format that algorithms can better understand. And label encoding assigns numerical labels to each category. Those sound like the right choices for this question.

upvoted 0 times

...

Nu

5 months ago

I'm a bit unsure about this one. I know one-hot encoding is used for categorical features, but I'm not sure about the other option.

upvoted 0 times

...

Bev

5 months ago

Hmm, this seems straightforward. I think one-hot encoding and label encoding are the two techniques I should use here.

upvoted 0 times

...

Twana

7 months ago

Normalization? Scaling? What is this, a beauty pageant? Give me those one-hot and label encoders any day! Let's keep it simple, folks.

upvoted 0 times

Kristel

5 months ago

Normalization and scaling can be useful too, but sometimes simpler is better.

upvoted 0 times

...

Phillip

6 months ago

I agree, one-hot encoding and label encoding are the way to go.

upvoted 0 times

...

Markus

6 months ago

Normalization and scaling can be useful too, but sometimes simpler is better.

upvoted 0 times

...

Latrice

7 months ago

I agree, one-hot encoding and label encoding are the way to go.

upvoted 0 times

...

Dustin

8 months ago

I'm with Elza on this one. B and D are the way to go. Although, I do like Ellen's 'make everything linear' approach. Sounds like a real time-saver!

upvoted 0 times

Jess

7 months ago

I'm not sure about linearization, but I definitely think B and D are the way to go.

upvoted 0 times

...

Val

7 months ago

I think Ellen's idea of linearizing everything is interesting, but I still prefer B and D.

upvoted 0 times

...

Elin

7 months ago

I agree with Elza, B and D are the best choices.

upvoted 0 times

...

Ellen

8 months ago

Hmm, I was thinking C and F. Linearization and pivoting, you know? Who needs all that fancy one-hot stuff when you can just make everything linear, right? *wink wink*

upvoted 0 times

...