MLS-C01 Modeling Practice Question
Which TWO of the following are valid techniques to handle missing data in a dataset?
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between data preprocessing techniques (like imputation) and other unrelated techniques (like normalization or SMOTE), so the trap here is that candidates may confuse SMOTE or normalization as valid missing data handling methods because they are common preprocessing steps, but they serve entirely different purposes.
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
✓
Mean imputation
Mean imputation (Option C) is a valid technique for handling missing data because it replaces missing values with the mean of the observed values for that feature, preserving the overall mean of the dataset. This approach is simple and effective for numerical data that is missing completely at random (MCAR), as it does not introduce bias in the mean estimate.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normalizing the data
Why it's wrong here
Normalization does not handle missing values.
- ✗
Adding a constant value of 0
Why it's wrong here
Adding 0 can bias the model.
- ✓
Mean imputation
Why this is correct
Replacing missing values with the mean is a standard technique.
- ✗
Synthetic Minority Over-sampling (SMOTE)
Why it's wrong here
SMOTE is for class imbalance.
- ✓
Deleting rows with missing values
Why this is correct
Deleting rows is valid if missingness is random.
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