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CompTIA DA0-001 Exam - Topic 4 Question 67 Discussion

Actual exam question for CompTIA's DA0-001 exam
Question #: 67
Topic #: 4
[All DA0-001 Questions]

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?

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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.

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Paulina
4 days ago
I remember practicing a question about dimensionality reduction, maybe PCA could help here?
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Zana
10 days 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
15 days 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
20 days 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
25 days 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
1 month 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
1 month 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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