CompTIA DY0-001 Exam - Topic 3 Question 9 Discussion

Actual exam question for CompTIA's DY0-001 exam

Question #: 9
Topic #: 3

A data scientist receives an update on a business case about a machine that has thousands of error codes. The data scientist creates the following summary statistics profile while reviewing the logs for each machine:

Which of the following is the most likely concern with respect to data design for model ingestion?

ASparse matrix

BGranularity misalignment

CInsufficient features

DMultivariate outliers

Show Suggested Answer

Suggested Answer: A

With 19,000 possible error-code features and each machine reporting only a handful (median of 7), your feature matrix will be extremely sparse (most entries zero) which can negatively impact both storage and model performance unless you address it (e.g., via sparse data structures or dimensionality reduction).

by Helene at Aug 01, 2025, 02:01 PM

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Vivienne

2 months ago

Wait, multivariate outliers? That sounds complicated!

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Abel

2 months ago

Insufficient features? Not sure about that one.

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Yun

3 months ago

I think granularity misalignment could be an issue too.

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Deeanna

3 months ago

I agree, sparse matrix seems like the most likely problem here.

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Mozell

3 months ago

Sparse matrix is definitely a concern with that many error codes.

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Carli

3 months ago

I recall a practice question about multivariate outliers, but I’m not convinced that’s the main issue in this scenario.

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Erick

4 months ago

Insufficient features sounds familiar, but I feel like with thousands of error codes, there should be enough data to work with.

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Keva

4 months ago

I'm not entirely sure, but I think granularity misalignment could be an issue if the error codes aren't consistent across machines.

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Sueann

4 months ago

I remember we discussed sparse matrices in class, especially how they can complicate model training. That might be a concern here.

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Jerry

4 months ago

Multivariate outliers seem like a potential concern here. With so many error codes, there could be some unusual combinations that could skew the model if not handled properly.

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Portia

4 months ago

Okay, let's see. Based on the information provided, I think the sparse matrix could be the biggest issue. With thousands of error codes, there's likely to be a lot of sparsity in the data, which could cause problems for model ingestion.

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Frederica

5 months ago

I'm a bit confused by this question. The summary statistics don't seem to give me enough information to determine the most likely concern. I'll have to think this through carefully.

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Mauricio

5 months ago

Hmm, this looks like a tricky one. I'm thinking the most likely concern is granularity misalignment, since the data seems to be summarized at a high level and that could cause issues when trying to build a model.

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Crista

6 months ago

I think sparse matrix could also be a concern, as it may lead to issues with data processing.

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Eladia

6 months ago

I believe insufficient features could also be a concern, as it may affect the accuracy of the model.

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Svetlana

6 months ago

I agree with Vi, granularity misalignment could be a problem for model ingestion.

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Vi

7 months ago

I think the most likely concern is granularity misalignment.

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Haydee

7 months ago

Insufficient features? Nah, mate. This data is drowning in features. It's like a data scientist's version of 'too much of a good thing'.

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Bernardine

7 months ago

Multivariate outliers, for sure. These error codes are all over the place. It's like trying to herd cats in a tornado.

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Edna

7 months ago

Whoa, that data looks like a mess! Granularity misalignment is definitely the culprit here. The machine needs to get its act together and start speaking our language.

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Jodi

6 months ago

I agree, granularity misalignment can really throw off the accuracy of the model.

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