This AI0-001 practice question tests your understanding of ai models and data engineering. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Use k-fold cross-validation
Why wrong: Cross-validation is a model evaluation technique.
Apply log transformation to all features
Why wrong: Not all features are skewed.
Normalize or standardize the features
Scaling improves convergence of gradient descent.
One-hot encode the features
Why wrong: Features are numeric, not categorical.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
One-hot encode the features
Why it's wrong here
Features are numeric, not categorical.
Common exam traps
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.
Detailed technical explanation
Under the hood, gradient descent updates weights using the formula w = w - α * (1/m) * X^T * (Xw - y), where the gradient magnitude is proportional to feature values. Without scaling, the loss surface becomes highly elongated, causing the optimizer to oscillate or require a very small learning rate. In practice, standardizing to zero mean and unit variance (Z-score) is often preferred over min-max scaling when features have outliers, as it is less sensitive to extreme values.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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FAQ
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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