Courseiva
Question 703 of 619
Fundamentals of AI and MLmediumMultiple SelectObjective-mapped

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.

About these practice questions

Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →

How Courseiva writes practice questions · Editorial policy

Last reviewed: Jun 25, 2026

Question Discussion

Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.

Loading comments…

Sign in to join the discussion.

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.