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Snowflake DSA-C02 Exam - Topic 2 Question 57 Discussion

How do you handle missing or corrupted data in a dataset?
D) All of the above
A) Drop missing rows or columns
B) Replace missing values with mean/median/mode
C) Assign a unique category to missing values

Snowflake DSA-C02 Exam - Topic 2 Question 57 Discussion

Actual exam question for Snowflake's DSA-C02 exam
Question #: 57
Topic #: 2
[All DSA-C02 Questions]

How do you handle missing or corrupted data in a dataset?

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Suggested Answer: D

by Melina at May 16, 2026, 07:54 PM

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Filiberto
3 days ago
Dropping rows is quick but can lose important info.
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Nathalie
8 days ago
Wait, can replacing with mean really skew results?
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Latrice
13 days ago
All of the above sounds like the safest bet!
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Krissy
18 days ago
I think assigning a category is underrated.
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Jimmie
23 days ago
I usually go for replacing with mean, it’s simple!
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Brynn
29 days ago
Dropping rows is quick but can lose important info.
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Lorrine
1 month ago
Wasn't there a practice question where we had to choose between these options? I think all of them could be valid in different scenarios.
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Gregg
1 month ago
I feel like assigning a unique category to missing values could be useful, especially in categorical datasets.
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Jaclyn
1 month ago
I think replacing missing values with the mean is common, but it might skew the data if there are outliers.
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Roxane
2 months ago
I remember we discussed dropping rows or columns, but I'm not sure if that's always the best approach.
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Starr
2 months ago
I’ve seen practice questions where all options were valid, so maybe D is the safest choice here?
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Linwood
2 months ago
I feel like assigning a unique category to missing values could be useful, but I’m not confident about when to use it.
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Mica
2 months ago
I think replacing missing values with the mean is common, but what if the data is skewed?
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Leeann
4 months ago
I remember we discussed dropping rows or columns, but I’m not sure if that’s always the best option.
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