MLS-C01 Exploratory Data Analysis Practice Question
Which TWO of the following are appropriate methods for handling missing data in a dataset?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Multiple imputation
Multiple imputation and using algorithms that handle missing values (e.g., XGBoost) are valid. Listwise deletion reduces sample size. Mean imputation may bias distributions. Dropping features with many missing values may lose information.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Dropping features with more than 50% missing values
Why it's wrong here
Dropping features may discard useful information; imputation is often preferred.
- ✗
Mean imputation for all features
Why it's wrong here
Mean imputation can distort relationships and reduce variance.
- ✓
Multiple imputation
Why this is correct
Multiple imputation accounts for uncertainty by creating multiple datasets.
- ✓
Using algorithms that handle missing values internally (e.g., XGBoost)
Why this is correct
Some tree-based algorithms can handle missing values by learning split directions.
- ✗
Listwise deletion (removing rows with missing values)
Why it's wrong here
Listwise deletion can reduce sample size significantly and introduce bias.
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Variation 1. Which TWO of the following are appropriate techniques for handling missing data during exploratory data analysis? (Select TWO.)
medium- A.Ignore missing values and proceed with modeling
- B.Replace missing values with -1 to indicate missing
- ✓ C.Impute missing values using mean or median for numerical features
- ✓ D.Visualize the missing data pattern using heatmaps or bar charts
- E.Delete all rows with any missing values
Why C: Options C and D are correct. Imputing missing values using mean or median for numerical features (C) is a common technique during EDA to preserve data size. Visualizing the missing data pattern with heatmaps or bar charts (D) helps understand the distribution and mechanism of missingness. Option A is incorrect because ignoring missing values can introduce bias and lead to inaccurate models. Option B is incorrect because replacing with -1 may distort the data distribution and is not a standard practice. Option E is incorrect because deleting all rows with missing values can cause significant data loss, especially if missingness is not random.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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