AI0-001 AI Models and Data Engineering Practice Question
A data science team is building a model to predict customer churn. The dataset includes categorical variables like 'region' and 'subscription_type'. Which three preprocessing steps should be applied to these categorical features? (Select THREE).
⚠ Common exam trap
CompTIA often tests the distinction between ordinal and nominal categorical variables, trapping candidates who apply label encoding to nominal data or one-hot encoding to ordinal data without considering the feature's inherent order.
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
✓
Label encoding
Label encoding (B) is correct because it converts each unique category in a categorical variable into a unique integer, which is a simple and memory-efficient way to prepare categorical data for machine learning models. Ordinal encoding (D) is correct for categorical variables with a natural order, such as 'subscription_type' if tiers exist (e.g., basic, premium, enterprise), preserving ordinal relationships. One-hot encoding (E) is correct for nominal categorical variables like 'region' where no order exists, creating binary columns for each category to avoid implying false ordinality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normalization
Why it's wrong here
Normalization rescales numerical values to a range, not for categorical features.
- ✓
Label encoding
Why this is correct
Label encoding assigns integers to each category, suitable for ordinal categories.
- ✗
Standard scaling
Why it's wrong here
Standard scaling is for continuous numerical features, not categorical.
- ✓
Ordinal encoding
Why this is correct
Ordinal encoding explicitly maps categories to numbers respecting order, similar to label encoding.
- ✓
One-hot encoding
Why this is correct
One-hot encoding creates binary columns for each category, suitable for nominal categories.
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JA
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.