MLA-C01 Data Preparation for Machine Learning Practice Question
A machine learning engineer is building a regression model to predict house prices. The feature 'square_footage' has values ranging from 500 to 10,000, while 'num_bedrooms' ranges from 1 to 10. Which preprocessing step is most critical before training a model that uses gradient descent?
⚠ Common exam trap
AWS often tests the distinction between scaling for gradient-based optimizers versus other preprocessing steps like encoding or transformation, trapping candidates who confuse feature scaling with handling outliers or categorical data.
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
✓
Standardize both features to have zero mean and unit variance.
Gradient descent is sensitive to the scale of features because it updates weights proportionally to the feature values. With 'square_footage' (500–10,000) and 'num_bedrooms' (1–10), the large range difference causes the loss function's contours to be elongated, leading to slow or unstable convergence. Standardizing both features to zero mean and unit variance ensures each feature contributes equally to the gradient updates, enabling faster and more reliable optimization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Standardize both features to have zero mean and unit variance.
Why this is correct
Standardization brings features to a common scale, crucial for gradient descent.
- ✗
Apply a logarithmic transformation to both features.
Why it's wrong here
Log transformation addresses skew, not scale differences across features.
- ✗
Encode the 'num_bedrooms' feature using one-hot encoding.
Why it's wrong here
One-hot encoding is for nominal categorical data, not for ordinal or continuous.
- ✗
Impute missing values using the mean of the feature.
Why it's wrong here
Missing values are not stated; the primary issue is scale.
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