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CompTIA DY0-001 Exam - Topic 3 Question 9 Discussion

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?
A) Sparse matrix
B) Granularity misalignment
C) Insufficient features
D) Multivariate outliers

CompTIA DY0-001 Exam - Topic 3 Question 9 Discussion

Actual exam question for CompTIA's DY0-001 exam
Question #: 9
Topic #: 3
[All DY0-001 Questions]

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?

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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
9 months ago
Wait, multivariate outliers? That sounds complicated!
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Abel
9 months ago
Insufficient features? Not sure about that one.
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Yun
9 months ago
I think granularity misalignment could be an issue too.
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Deeanna
10 months ago
I agree, sparse matrix seems like the most likely problem here.
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Mozell
10 months ago
Sparse matrix is definitely a concern with that many error codes.
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Carli
10 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
10 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
11 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
11 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
11 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
11 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
11 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
11 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
1 year ago
I think sparse matrix could also be a concern, as it may lead to issues with data processing.
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Eladia
1 year ago
I believe insufficient features could also be a concern, as it may affect the accuracy of the model.
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Svetlana
1 year ago
I agree with Vi, granularity misalignment could be a problem for model ingestion.
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Vi
1 year ago
I think the most likely concern is granularity misalignment.
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Haydee
1 year 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
1 year 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
1 year 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
1 year ago
I agree, granularity misalignment can really throw off the accuracy of the model.
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