MLA-C01 Data Preparation for Machine Learning Practice Question
A team is building a regression model on a dataset with missing values in multiple features. They decide to use a k-Nearest Neighbors (k-NN) imputer. The dataset has 100,000 rows and 50 features. Which step should the team take to ensure the imputation is efficient and accurate?
⚠ Common exam trap
AWS often tests the misconception that k-NN imputation works directly on raw data without preprocessing, trapping candidates who overlook the scale sensitivity of distance-based algorithms.
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 the features before applying k-NN imputation
Standardizing features before applying k-NN imputation is critical because k-NN relies on distance calculations (e.g., Euclidean distance). If features are on different scales (e.g., one feature ranges 0–1 and another 0–100,000), the distance metric will be dominated by the larger-scale feature, leading to biased neighbor selection and inaccurate imputation. Standardization (e.g., z-score scaling) ensures each feature contributes equally to the distance computation, improving both efficiency and accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set k=1 to minimize bias
Why it's wrong here
k=1 is prone to overfitting and noise.
- ✗
Use all 100,000 rows to find neighbors for each missing value
Why it's wrong here
Computationally expensive; consider sampling or approximate methods.
- ✓
Standardize the features before applying k-NN imputation
Why this is correct
Ensures distance is equally weighted across features.
- ✗
Use only the feature with missing values to find neighbors
Why it's wrong here
Does not use information from other features.
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