Question 703 of 619
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
Imputing with the mean is a common technique for handling missing values in numerical features because it preserves the overall distribution of the data without reducing the dataset size. This method replaces each missing entry with the arithmetic mean of the non-missing values in that column, which is simple to implement and works well when data is missing completely at random (MCAR).
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 is for converting categories to numbers.
- ✗
Normalization
Why it's wrong here
Normalization scales features, it does not handle missing values.
- ✓
Imputing with mean
Why this is correct
Mean imputation replaces missing values with the mean of the column.
- ✓
Dropping rows with any missing values
Why this is correct
Removing rows with missing values is a simple approach.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding is for categorical variables, not for missing values.
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Last reviewed: Jun 25, 2026
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.
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