Deal of The Day! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions
Mail Us support@pass4success.com
Location US
Mob_nav_barsMENU

CompTIA Exam DY0-001 Topic 3 Question 2 Discussion

Actual exam question for CompTIA's DY0-001 exam
Question #: 2
Topic #: 3
[All DY0-001 Questions]

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.)

Show Suggested Answer Hide 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
Dustin
14 days 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
4 days ago
I'm not sure about linearization, but I definitely think B and D are the way to go.
upvoted 0 times
...
Val
5 days ago
I think Ellen's idea of linearizing everything is interesting, but I still prefer B and D.
upvoted 0 times
...
Elin
6 days ago
I agree with Elza, B and D are the best choices.
upvoted 0 times
...
...
Ellen
19 days 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
...
Elza
22 days ago
Definitely B and D. One-hot encoding to create binary columns, and label encoding to turn the categories into numerical values. Easy peasy!
upvoted 0 times
Marisha
9 days ago
I agree, one-hot encoding is great for creating binary columns.
upvoted 0 times
...
...
Jeniffer
23 days ago
I'm not sure about Label encoding. I think Normalization and Scaling would be more useful for building a model.
upvoted 0 times
...
Stefanie
1 months ago
I agree with Ona. One-hot encoding helps with categorical variables and Label encoding assigns a unique numerical value to each category.
upvoted 0 times
...
Ona
1 months ago
I think the data scientist should use One-hot encoding and Label encoding.
upvoted 0 times
...
Corinne
1 months ago
I'm pretty sure one-hot encoding and label encoding are the way to go here. Normalizing and scaling won't help with categorical features.
upvoted 0 times
Terrilyn
30 days ago
Pivoting and linearization wouldn't be useful for transforming categorical features.
upvoted 0 times
...
Ardella
1 months ago
Normalization and scaling are more for numerical features, not categorical ones.
upvoted 0 times
...
Kate
1 months ago
I agree, one-hot encoding and label encoding are the best choices for categorical features.
upvoted 0 times
...
...

Save Cancel