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MLA-C01 Practice Question: A data scientist is preparing a dataset for a…
A data scientist is preparing a dataset for a linear regression model. The features have different scales: one feature ranges from 0 to 1000, another from 0 to 1, and a third from -5 to 5. The scientist wants to ensure that all features contribute equally to the model. Which TWO scaling techniques should the scientist consider? (Select TWO.)
⚠ Common exam trap
MLA-C01 often tests the misconception that any preprocessing step (PCA, one-hot encoding, log transform) counts as scaling, when only MinMaxScaler and StandardScaler directly normalize feature ranges.
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
✓
MinMaxScaler (min-max normalization)
MinMaxScaler (A) is correct because it rescales each feature to a fixed range, typically [0, 1], using the formula (x - min) / (max - min), which directly addresses the differing ranges (0-1000, 0-1, -5-5) and puts all features on a comparable scale so they contribute equally to the linear regression. StandardScaler (E) is also correct because it standardizes features by removing the mean and scaling to unit variance using z = (x - μ) / σ, which is a standard and effective way to equalize feature contributions when scales differ. PCA (B) is not a scaling technique; it is a dimensionality-reduction method and does not by itself normalize feature ranges. One-hot encoding (C) is for converting categorical variables into binary vectors, not for rescaling numeric features. Log transformation (D) is a nonlinear transform that can reduce skew but does not guarantee equal scaling across features with different ranges.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
MinMaxScaler (min-max normalization)
Why this is correct
MinMaxScaler rescales each feature to a fixed 0–1 range by subtracting the minimum and dividing by the range, so the 0–1000 feature no longer dominates the 0–1 and -5–5 features. This equalises their contribution to the linear regression, satisfying the equal-contribution constraint in the stem.
- ✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA is a dimensionality-reduction technique that projects features onto principal components; it does not rescale each feature onto a common range. It is tempting because it is often applied alongside scaling in preprocessing pipelines, and it would be correct when the goal is to remove correlated features rather than equalise their contribution.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding converts categorical variables into binary indicator columns; it does not rescale continuous numeric ranges, so the 0–1000 feature would still dominate the regression coefficients. It is tempting because it genuinely solves nominal-category handling, and would be correct if a feature were an unordered label such as colour or region.
- ✗
Log transformation
Why it's wrong here
Log transformation compresses skewed distributions but does not bound features to a common range, and it fails on the negative-valued feature entirely. It is tempting because it reduces the influence of extreme values, and would be correct for heavily right-skewed positive data such as income or counts.
- ✓
StandardScaler (z-score normalization)
Why this is correct
StandardScaler centres each feature on zero and divides by its standard deviation, producing comparable z-scores regardless of the original 0–1000, 0–1 and -5–5 ranges. This equalises each feature's influence on the linear regression coefficients, meeting the stem's equal-contribution requirement.
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