AI0-001 AI Concepts and Foundations Practice Question
Which TWO techniques are commonly used to handle missing data in a dataset?
⚠ Common exam trap
CompTIA often tests the distinction between data preprocessing techniques that handle missing values versus those that transform or reduce features, so candidates may confuse feature scaling or PCA with missing data handling because they are all part of data preparation.
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
✓
Remove rows with missing values
Removing rows with missing values is a straightforward technique to handle missing data, especially when the missingness is random and the dataset is large enough that dropping a few rows does not significantly reduce the sample size or introduce bias. Option D is correct because imputing missing values with the mean or median is a common statistical method that preserves the dataset size and is simple to implement, though it can reduce variance and may distort relationships if the data is not missing completely at random.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Feature scaling
Why it's wrong here
Scaling does not address missing values.
- ✗
One-hot encoding
Why it's wrong here
Encoding is for categorical features, not missing values.
- ✓
Remove rows with missing values
Why this is correct
Simple deletion if missing data is minimal.
- ✓
Impute with mean or median
Why this is correct
Fills missing values with central tendency.
- ✗
Principal component analysis (PCA)
Why it's wrong here
PCA reduces dimensionality, not imputes missing data.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.