Question 963 of 835
MLA-C01 Data Preparation for Machine Learning Practice Question
A machine learning engineer is preparing a dataset for a multiclass classification task. The dataset has 10 features and 100,000 rows. Which TWO techniques should the engineer use to reduce the risk of overfitting during data preparation?
⚠ Common exam trap
AWS often tests the distinction between techniques that address overfitting versus those that handle other data issues like imbalance or skewness, leading candidates to confuse SMOTE or log transforms as overfitting remedies.
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
✓
Data augmentation (e.g., adding noise)
Data augmentation (A) is correct because it artificially increases the diversity of the training set by adding noise or transformations, which helps the model generalize better and reduces overfitting. Feature selection using correlation analysis (E) is correct because it removes redundant or highly correlated features, simplifying the model and minimizing the risk of learning noise from irrelevant predictors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Data augmentation (e.g., adding noise)
Why this is correct
Increases training data diversity, reducing overfitting.
- ✗
SMOTE to balance classes
Why it's wrong here
Addresses class imbalance, not general overfitting.
- ✗
One-hot encoding of all categorical features
Why it's wrong here
Increases dimensionality, potentially worsening overfitting.
- ✗
Log transformation of skewed features
Why it's wrong here
Addresses skewness, not overfitting.
- ✓
Feature selection using correlation analysis
Why this is correct
Removes irrelevant/redundant features, reducing complexity.
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Last reviewed: Jun 30, 2026
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