CompTIA DY0-001 Exam - Topic 4 Question 13 Discussion

Actual exam question for CompTIA's DY0-001 exam

Question #: 13
Topic #: 4

A data scientist is standardizing a large data set that contains website addresses. A specific string inside some of the web addresses needs to be extracted. Which of the following is the best method for extracting the desired string from the text data?

ARegular expressions

BNamed-entity recognition

CLarge language model

DFind and replace

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Suggested Answer: A

by Heidy at Nov 22, 2025, 11:59 AM

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Tina

3 months ago

Regular expressions are definitely the way to go for accuracy!

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Jaleesa

3 months ago

D) Find and replace is too simple for this kind of data.

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Fredric

3 months ago

B) Named-entity recognition sounds interesting, but it’s not specific enough for this task.

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Rashad

3 months ago

Agreed! Regular expressions can handle complex patterns easily.

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Leana

3 months ago

Find and replace? That's too basic for this task!

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Gladys

4 months ago

Wait, can a large language model really do this? Sounds iffy.

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Karan

4 months ago

Definitely going with A, regular expressions are the best!

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Jaclyn

4 months ago

I think named-entity recognition could work too.

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Janna

5 months ago

Regular expressions are perfect for this!

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Lajuana

5 months ago

Option A all the way. Regular expressions are the text-wrangling superpower you need to tame those website addresses.

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Gerri

5 months ago

Haha, "large language model" - that's like using a sledgehammer to crack a nut! Regular expressions are the surgical tool you need for this job.

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Maryann

5 months ago

Regular expressions are the clear choice. They're powerful, flexible, and perfect for this kind of targeted string extraction.

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Yuonne

5 months ago

I'd go with option A. Regular expressions are the Swiss Army knife of text processing - they can handle just about any extraction task.

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Raymon

5 months ago

Large language models seem powerful, but I doubt they are the best fit for just extracting a specific string from a URL. Regular expressions seem more straightforward.

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Blossom

6 months ago

I feel like I’ve seen a similar question before, and I think find and replace could work, but it might not be as flexible as regex for this task.

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Ernie

6 months ago

I'm not entirely sure, but I remember practicing with named-entity recognition for extracting information. It seems more suited for structured data though.

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Tiera

6 months ago

I think regular expressions might be the best choice here since they are specifically designed for pattern matching in strings.

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Fredric

6 months ago

I'm a bit confused by this question. I know regular expressions can be powerful, but I'm not sure if they're the best approach compared to the other options. Maybe I should research named-entity recognition or large language models a bit more before deciding.

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Scarlet

6 months ago

Regular expressions are definitely the way to go here. I've used them before for text extraction tasks, and I'm confident I can write a regex that will reliably pull out the desired string from the web addresses.

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Jade

6 months ago

Hmm, this is a tricky one. I think I'd start by trying a simple find and replace, but that might not be robust enough for a large data set. Regular expressions could be the way to go, but I'd need to make sure I understand the syntax well.

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Alexia

7 months ago

I think A) Regular expressions is the best choice. It's precise for text extraction.

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Belen

7 months ago

Regular expressions are definitely the way to go here. They're like a secret language for text manipulation!

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Lili

7 months ago

C) Large language model is overkill for just extracting strings.

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Leota

8 months ago

I'm a bit unsure about this one. I know regular expressions can be powerful, but I'm not sure if they're the best method compared to the other options. Maybe I should consider named-entity recognition or a language model as well.

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Trinidad

8 months ago

Regular expressions seem like the best approach here. I'm pretty confident I can use a well-crafted regex to extract the specific string from the web addresses.

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