CompTIA DA0-001 Exam - Topic 4 Question 67 Discussion

Actual exam question for CompTIA's DA0-001 exam

Question #: 67
Topic #: 4

Which option best concepts should be applied if a data set with 40 fields needs to be pared down to 20 fields and contains similar data across multiple fields?

BConsolidation

CCompliance

DStandardization

ADuplication

Show Suggested Answer

Suggested Answer: B

Consolidation is the process of combining multiple elements into a single, more effective or coherent whole. In the context of data analytics, consolidation would involve merging similar fields to reduce the overall number of fields in a dataset. This is particularly useful when a dataset contains redundant or similar data across multiple fields, as it helps to simplify the data structure and improve efficiency. Techniques such as dimensionality reduction are often applied to achieve this, where the goal is to retain the most informative and representative features of the data while reducing the number of total features.

Applied Dimensionality Reduction --- 3 Techniques using Python1.

Seven Techniques for Data Dimensionality Reduction2.

Best practices when working with datasets3.

Effectively Handling Large Datasets4.

by Sarina at Jan 23, 2026, 07:12 AM

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Celeste

15 days ago

Wait, are we sure the data is that similar? Sounds sketchy!

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Shawnee

21 days ago

I agree, but we should consider the importance of each field too.

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Sonia

26 days ago

Not sure if 20 fields is enough, though.

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Sarah

1 month ago

I think PCA could really help here.

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Noel

1 month ago

Definitely look into feature selection techniques!

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Sonia

1 month ago

Wait, we're supposed to actually know how to solve this? I was just going to guess randomly and hope for the best.

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Kayleigh

2 months ago

Haha, this question is a piece of cake! Just throw some machine learning magic at it and call it a day.

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Tresa

2 months ago

Hmm, I'd probably try to identify redundant fields and combine them using techniques like data aggregation or dimensionality reduction.

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Reita

2 months ago

I'd say feature selection methods like correlation-based or wrapper-based selection would be the way to go.

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Vince

2 months ago

Definitely go with data reduction techniques like principal component analysis or factor analysis.

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Emily

2 months ago

This reminds me of a similar practice question where we had to use decision trees to determine feature importance. That might be relevant here too.

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Dominque

2 months ago

I feel like we should consider correlation analysis to identify which fields are redundant, but I can't recall the exact steps.

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Paulina

3 months ago

I remember practicing a question about dimensionality reduction, maybe PCA could help here?

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Zana

3 months ago

I think we might need to look at feature selection techniques, but I'm not entirely sure which ones would be best for this scenario.

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Zack

4 months ago

Whew, 40 fields to 20 - that's a lot of paring down. I'd start by talking to the stakeholders to understand the key use cases and priorities.

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Krystina

4 months ago

Sounds like a good opportunity to use some feature selection techniques. I'd look into methods like recursive feature elimination or LASSO regression.

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Wade

4 months ago

Okay, let's see. I'd want to analyze the data distribution and variance across the fields to find the most informative subset.

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Tricia

4 months ago

Hmm, this seems tricky. I'd probably try to group the fields by similarity and then select the most representative one from each group.

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Laura

4 months ago

I'd start by looking for fields with high correlation or redundancy, then try to identify the most important 20 based on the data's purpose.

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