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MLA-C01 Practice Question: A data engineer needs to perform feature…
A data engineer needs to perform feature selection on a dataset with 500 numeric features to train a regression model. The engineer wants to remove features that are redundant or have low predictive power. Which TWO techniques should the engineer consider? (Select TWO.)
⚠ Common exam trap
MLA-C01 often tests the difference between preprocessing (standardization, one-hot, oversampling) and feature selection (correlation, Lasso) — candidates pick standardization or one-hot thinking they reduce features, when they actually transform or expand them.
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
✓
Correlation analysis
Correlation analysis (D) is correct because it identifies pairs of numeric features whose values move together, letting the engineer drop redundant, highly correlated predictors before training the regression model. Lasso regularization (E) is correct because its L1 penalty shrinks the coefficients of weak or irrelevant features exactly to zero, effectively performing embedded feature selection on the 500 numeric inputs. Oversampling (A) is a class-imbalance technique that duplicates or synthesizes minority-class samples and does nothing to remove redundant or low-predictive-power features. Standardization (B) merely rescales features to comparable ranges and does not eliminate any feature. One-hot encoding (C) is a categorical-variable transformation that expands categories into binary columns and is irrelevant to a dataset of numeric features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Oversampling
Why it's wrong here
Oversampling duplicates or synthesises minority-class rows to rebalance the target distribution; it alters class proportions, not the feature set, so no predictor is removed. It tempts because it is a legitimate remedy for severe class imbalance in classification, which is a different problem from the regression feature-selection task described.
- ✗
Standardization
Why it's wrong here
Standardization rescales feature magnitudes; it removes neither redundant features nor those with low predictive power. It is tempting because it is a standard preprocessing step before regression, and it would be correct when features have differing scales that distort distance-based or regularised models.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding expands categorical variables into binary indicator columns, increasing dimensionality rather than eliminating redundant numeric predictors. It tempts because it is essential preprocessing when a model cannot consume raw categorical values, but the stem specifies 500 numeric features and a regression target.
- ✓
Correlation analysis
Why this is correct
Correlation analysis identifies pairs of numeric features whose values move together, exposing redundancy so one of each highly correlated pair can be dropped. It directly satisfies the stem's requirement to remove redundant features, and with 500 numeric columns it scales cheaply, unlike wrapper methods that retrain the model per subset.
- ✓
Lasso regularization (L1)
Why this is correct
Lasso (L1) adds a penalty proportional to the absolute value of coefficients, shrinking irrelevant or redundant feature weights exactly to zero. That produces sparse models, so it performs embedded feature selection across the 500 numeric features while training the regression model.
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Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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