MLS-C01 Modeling Practice Question

Which TWO actions are valid ways to handle missing data in a dataset before training a machine learning model? (Select TWO.)

⚠ Common exam trap

The MLS-C01 exam often tests the misconception that 'ignoring missing values' is acceptable because some algorithms like tree-based models can technically handle missing values internally, but the exam expects explicit data preprocessing steps as part of the modeling pipeline.

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

✓

Delete rows with missing values

Deleting rows with missing values (listwise deletion) is a straightforward and valid approach when the missing data is random and the dataset is large enough that the loss of rows does not significantly reduce statistical power or introduce bias. This method avoids the need to estimate missing values and is commonly used in practice when the proportion of missing data is low.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Delete rows with missing values
Why this is correct
Row deletion is valid if missingness is random.
✗
Remove all features that have any missing values
Why it's wrong here
Too aggressive; may lose useful features.
✗
Replace missing values with the maximum value
Why it's wrong here
Not standard practice.
✗
Ignore missing values and train the model
Why it's wrong here
Most algorithms cannot handle missing values.
✓
Impute missing values with the mean
Why this is correct
Mean imputation is common.

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MLS-C01 Modeling Practice Question

Why each option matters

Option-by-option breakdown

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