AI0-001 AI Models and Data Engineering Practice Question
An engineer is building a regression model to predict housing prices. The dataset includes features such as square footage, number of bedrooms, and year built. The engineer notices that the square footage values range from 500 to 10,000, while the number of bedrooms ranges from 1 to 5. Which preprocessing step is most critical before training a gradient descent-based model?
⚠ Common exam trap
CompTIA often tests the misconception that any data transformation (like log or one-hot encoding) is universally beneficial, but the key is matching the preprocessing step to the model's mathematical requirements—here, gradient descent's sensitivity to scale makes normalization/standardization the critical step.
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
✓
Normalize or standardize the features
Gradient descent-based models are sensitive to the scale of input features because they update weights proportionally to the gradient, which is influenced by feature magnitudes. With square footage ranging 500–10,000 and bedrooms 1–5, the larger feature will dominate the gradient, causing slow or unstable convergence. Normalizing or standardizing (e.g., Z-score or min-max scaling) ensures all features contribute equally, leading to faster and more reliable training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use k-fold cross-validation
Why it's wrong here
Cross-validation is a model evaluation technique.
- ✗
Apply log transformation to all features
Why it's wrong here
Not all features are skewed.
- ✓
Normalize or standardize the features
Why this is correct
Scaling improves convergence of gradient descent.
- ✗
One-hot encode the features
Why it's wrong here
Features are numeric, not categorical.
About these practice questions
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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.