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CompTIA DA0-001 Exam - Topic 4 Question 57 Discussion

Actual exam question for CompTIA's DA0-001 exam
Question #: 57
Topic #: 4
[All DA0-001 Questions]

A healthcare data analyst notices that one data set in the column for BloodPressure contains several outliers that need to be replaced with meaningful values. Which of the following data manipulation techniques should the analyst use?

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Suggested Answer: B

Comprehensive and Detailed In-Depth

In data analysis, handling outliers is crucial to ensure the accuracy and reliability of the dataset.Outliers can significantly skew statistical analyses and lead to misleading conclusions. One common method to address outliers isimputation, which involves replacing missing or anomalous data with substituted values based on other available information.

Option A:Recode

Rationale:Recoding involves changing the values of a variable to a different set of values, often to simplify categories or to correct data entry errors. While useful, recoding is not specifically aimed at addressing outliers.

Option B:Impute

Rationale:Imputation is the process of replacing missing or anomalous data points with substituted values, often derived from the dataset's statistical properties, such as the mean, median, or mode. This technique helps maintain the dataset's integrity by ensuring that analyses are not biased by missing or extreme values.


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Option C:Append

Rationale:Appending involves adding new data to the existing dataset, either by adding new rows (records) or columns (variables). This process does not address the issue of outliers within an existing column.

Option D:Reduction

Rationale:Reduction refers to decreasing the size or complexity of the dataset, such as by aggregating data or removing unnecessary variables. While it can help in simplifying data analysis, reduction does not specifically target the treatment of outliers.

by Vi at Apr 30, 2025, 12:36 PM

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Ronny
2 months ago
Wait, can you really just replace outliers like that?
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Esteban
2 months ago
I think recoding could work too, but imputation seems better.
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Verdell
2 months ago
Impute is the way to go for outliers!
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Abel
3 months ago
Reduction isn’t even relevant to this question.
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Hyman
3 months ago
Append? That doesn’t make sense here.
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Herminia
3 months ago
Reduction seems off for this scenario; I believe we need to focus on replacing values, so impute might be the way to go.
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Jame
3 months ago
I feel like impute is definitely the best option, but I might be mixing it up with recoding.
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Hollis
4 months ago
I remember practicing a question like this, and I think recoding is more about changing categories rather than replacing values.
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Bette
4 months ago
I think imputation is the right choice here since we need to replace outliers with meaningful values, but I'm not entirely sure.
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Kristin
4 months ago
This is a good data cleaning question. I'm pretty confident that Impute is the right choice here. Replacing outliers with meaningful values is exactly what Impute is designed for.
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Julio
4 months ago
I'm a little confused by the options here. Append doesn't seem relevant, and Reduction is more about reducing the data, not replacing outliers. I think I'll go with Impute, but I'm not 100% sure.
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Aliza
4 months ago
Okay, I've got this. The key is to replace the outliers, so Impute is definitely the way to go. That will let me substitute the outliers with appropriate values based on the rest of the data.
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My
5 months ago
Hmm, I'm a bit unsure about this one. Recode, Impute, and Reduction all sound like they could work for replacing outliers. I'll have to think this through carefully.
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Markus
5 months ago
This seems like a straightforward data cleaning question. I think I'll go with Impute to replace the outliers with meaningful values.
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Shalon
8 months ago
I think both options A) Recode and B) Impute could work, depending on the specific situation.
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Tricia
8 months ago
I would go with option A) Recode, as it involves changing the values to meaningful ones.
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Mari
9 months ago
Impute, impute, impute! It's the only way to maintain the integrity of the data set without introducing any weird artifacts.
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Laine
9 months ago
I agree with Carey, imputing seems like the best choice to replace outliers.
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Monte
10 months ago
Impute all the way! Reducing the data set or appending more information doesn't seem relevant to handling those outliers.
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Noel
8 months ago
Imputing is definitely the way to go in this situation. It will help maintain the integrity of the data set.
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Janna
8 months ago
I agree, using imputation will help replace those outliers with meaningful values.
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Weldon
8 months ago
Impute all the way! It's the best option for handling outliers in the BloodPressure column.
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Carey
10 months ago
I think the analyst should use option B) Impute.
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Kiley
10 months ago
I think imputing the missing values would be the best approach here. Recoding might not capture the true nature of the outliers.
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Tennie
8 months ago
Imputing the outliers will help maintain the integrity of the data set.
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Devon
8 months ago
I agree, recoding might not be the best option for outliers.
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Filiberto
9 months ago
Imputing the missing values is definitely the way to go.
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