Quick Answer
The correct step is to standardize the features before applying k-NN imputation. This is essential because k-NN imputation relies on distance calculations, such as Euclidean distance, to identify the nearest neighbors for filling missing values. Without standardization, features with larger numerical ranges—like a feature spanning 0 to 100,000—will dominate the distance metric, skewing neighbor selection and producing inaccurate imputations. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this concept tests your understanding of how preprocessing directly impacts model performance, often appearing in scenario-based questions about handling missing data. A common trap is assuming k-NN imputation works automatically on raw data, but the exam expects you to recognize that scale-sensitive algorithms require feature scaling first. Memory tip: think “scale before you k-neighbor” to avoid letting big numbers bully the distance metric.
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Set k=1 to minimize bias
Why wrong: k=1 is prone to overfitting and noise.
Use all 100,000 rows to find neighbors for each missing value
Why wrong: Computationally expensive; consider sampling or approximate methods.
Standardize the features before applying k-NN imputation
Ensures distance is equally weighted across features.
Use only the feature with missing values to find neighbors
Why wrong: Does not use information from other features.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
Use only the feature with missing values to find neighbors
Why it's wrong here
Does not use information from other features.
Common exam traps
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.
Detailed technical explanation
Under the hood, k-NN imputation computes distances (commonly Euclidean) between the row with a missing value and all other rows, then averages the k nearest neighbors' values for that feature. Standardization transforms each feature to have mean 0 and standard deviation 1, ensuring that no feature with a larger range (e.g., income in dollars) dominates the distance over a smaller-range feature (e.g., age in years). In real-world scenarios, such as medical datasets with mixed units (e.g., blood pressure in mmHg and cholesterol in mg/dL), failing to standardize can cause the imputer to ignore clinically relevant small-scale features entirely.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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FAQ
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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