DA0-002 Data Analysis Practice Question
A data analyst is preparing data for a k-nearest neighbors algorithm. The features include age (0-100) and income (0-200,000). Which technique should be applied to ensure the distance metric is not dominated by income?
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
✓
Min-max normalization
Min-max normalization scales features to a 0-1 range, ensuring each feature contributes equally to distance calculations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Min-max normalization
Why this is correct
Correct: min-max normalization scales to [0,1], preventing features with larger ranges from dominating.
- ✗
Log transformation
Why it's wrong here
Log transformation changes distribution but does not necessarily bound features equally.
- ✗
Z-score standardization
Why it's wrong here
Z-score standardization centers data around 0 with std 1, but normalization to 0-1 is typical for distance-based algorithms.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding is for categorical variables, not scaling.
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Written by Johnson Ajibi, MSc IT Security
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This DA0-002 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DA0-002 exam.