CompTIA Exam DY0-001 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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Crista

1 days ago

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

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Eladia

4 days ago

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

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Svetlana

5 days ago

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

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Vi

8 days ago

I think the most likely concern is granularity misalignment.

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Haydee

12 days 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

13 days 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

14 days 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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