AIF-C01 Fundamentals of AI and ML Practice Question
A data scientist is preparing data for a classification task. Which TWO techniques are commonly used for handling missing values? (Choose two.)
⚠ Common exam trap
The AIF-C01 exam often tests the distinction between data preprocessing techniques (e.g., encoding, scaling) and missing value handling, so candidates mistakenly select label encoding or normalization because they are common preprocessing steps, even though they do not address missing data.
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
✓
Imputing with mean
Replacing missing numeric entries with the column mean is a standard, simple imputation technique that preserves the dataset size and avoids discarding information. Option D (Dropping rows with any missing values) is also correct because listwise deletion is a common, straightforward approach to handling missing data, especially when the missingness is minimal or random. The other options do not address missing values: A (Label encoding) converts categorical labels into integer codes, B (Normalization) rescales numeric feature ranges, and E (One-hot encoding) creates binary indicator columns for categories—all are preprocessing steps for existing values, not missing-data handling.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Label encoding
Why it's wrong here
Label encoding converts categorical strings into integer codes; it neither detects nor fills missing values, so it cannot address the task. It is tempting because it is a standard preprocessing step for categorical features, and it would be the right choice when preparing nominal or ordinal columns for algorithms that require numeric input.
- ✗
Normalization
Why it's wrong here
Normalization scales features, it does not handle missing values.
- ✓
Imputing with mean
Why this is correct
Imputing with mean replaces missing numerical entries with the column average, preserving dataset size and avoiding dropped rows. This directly satisfies the stem's requirement for a common missing-value technique, since mean imputation is a standard preprocessing method in classification workflows.
- ✓
Dropping rows with any missing values
Why this is correct
Dropping rows with any missing values removes incomplete records entirely, satisfying the requirement to handle missing data. It is a standard preprocessing technique, though it reduces dataset size and risks discarding useful information when missingness is widespread or non-random across features.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding converts categorical variables into binary indicator columns; it does not address missing values. It is correct for encoding nominal categories before training. Handling missing values uses techniques such as mean or median imputation and deletion of incomplete rows.
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