AI0-001 AI Concepts and Foundations Practice Question
A retail company wants to build a model to predict customer churn based on purchase history and demographics. The dataset includes categorical features like region and gender, and numerical features like total spend. What is the best initial step before training the model?
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
✓
One-hot encode categorical variables and normalize numerical variables
One-hot encoding categorical variables and normalizing numerical variables is standard preprocessing to convert categorical data into numeric format and scale features, which many algorithms require for optimal performance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Train a deep neural network directly on raw data
Why it's wrong here
Deep neural networks require preprocessed data; raw data with mixed types cannot be fed directly.
- ✓
One-hot encode categorical variables and normalize numerical variables
Why this is correct
This is the correct initial step to prepare the data for most machine learning models.
- ✗
Remove all categorical features to simplify the model
Why it's wrong here
Removing categorical features discards valuable information and is not recommended.
- ✗
Perform principal component analysis (PCA) on all features
Why it's wrong here
PCA is a dimensionality reduction technique, not an initial preprocessing step; it should be applied after basic preprocessing.
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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.