DA0-002 Data Analysis Practice Question
Which TWO of the following are appropriate uses of min-max normalisation?
⚠ Common exam trap
DA0-002 often tests the confusion between min-max normalisation and z-score standardisation; candidates may incorrectly select the z-score description as a use of min-max.
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
✓
Scaling features to a range of 0 to 1
Option B is correct because min-max normalisation rescales each feature to a fixed range, typically [0, 1], using the formula (x − min)/(max − min), which is exactly its defining purpose. Option D is correct because k-nearest neighbours relies on distance calculations (e.g., Euclidean distance), so features on different scales would dominate the distance metric; min-max normalisation puts all features on a comparable 0–1 scale, improving the algorithm's behaviour. Option A is not a use of min-max normalisation but of standardisation (z-score scaling), which produces mean 0 and standard deviation 1. Option C is not specific to min-max normalisation; linear regression with normally distributed residuals concerns the error distribution, not feature scaling, and standardisation is more commonly associated with such assumptions. Option E is incorrect because missing values are handled by imputation or deletion techniques, not by min-max normalisation, which requires complete numeric data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Transforming data to have mean 0 and standard deviation 1
Why it's wrong here
Min-max normalisation rescales values to a fixed range, typically 0–1, so it cannot produce a mean of 0 with a standard deviation of 1 — that is z-score standardisation's output. It tempts because both are feature-scaling techniques applied before distance-based algorithms; z-score suits Gaussian data or algorithms assuming centred, unit-variance inputs.
- ✓
Scaling features to a range of 0 to 1
Why this is correct
Min-max normalisation linearly transforms each feature to a fixed 0–1 range using the minimum and maximum values, satisfying the requirement to bound features within a common scale. This suits algorithms sensitive to magnitude, such as k-nearest neighbours or neural networks, where unbounded inputs distort distance calculations.
- ✗
Preparing data for linear regression with normally distributed residuals
Why it's wrong here
Min-max normalisation rescales to a fixed [0,1] range and does not produce normally distributed residuals; linear regression assumes that distribution. It suits algorithms sensitive to feature magnitude, such as k-nearest neighbours or neural networks, not regression residual assumptions.
- ✓
Preparing data for k-nearest neighbours algorithm
Why this is correct
Min-max normalisation scales every feature to an identical 0–1 range, preventing attributes with larger magnitudes from dominating the Euclidean distance calculation that k-nearest neighbours relies on. This directly satisfies the stem's requirement, since distance-based algorithms demand comparable feature scales to produce meaningful neighbour rankings.
- ✗
Handling missing values
Why it's wrong here
Min-max normalisation rescales numeric values and cannot impute or represent absent data; missing values must be handled beforehand by deletion or imputation. It is appropriate for bounded numeric features, not for addressing gaps in a dataset.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.