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MLA-C01 Practice Question: A machine learning engineer needs to standardize…
A machine learning engineer needs to standardize features to have zero mean and unit variance before training a support vector machine. Which scaling method should they apply?
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
✓
StandardScaler
StandardScaler transforms data to have zero mean and unit variance, which is required for SVM and many other algorithms.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
StandardScaler
Why this is correct
StandardScaler subtracts the feature mean and divides by the standard deviation, producing zero mean and unit variance per feature. That is exactly the transformation requested, and it suits SVMs because they rely on distances and are sensitive to differing feature scales; MinMaxScaler would only bound values to a range.
- ✗
Normalizer
Why it's wrong here
Normalizer scales each individual sample to unit norm across features, adjusting rows rather than columns, so it cannot produce zero mean and unit variance per feature. It suits text or cosine-similarity pipelines where sample direction matters, not the StandardScaler-style column standardisation an SVM requires.
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RobustScaler
Why it's wrong here
RobustScaler centres using the median and scales by the interquartile range, so it does not produce zero mean and unit variance. It is tempting because it handles outliers, and would be correct for skewed data where extreme values would distort StandardScaler's mean and standard deviation.
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MinMaxScaler
Why it's wrong here
MinMaxScaler rescales each feature to a fixed [0,1] range using its minimum and maximum, so the resulting distribution keeps its original shape rather than gaining zero mean and unit variance. It suits algorithms needing bounded inputs, such as neural networks, not the Gaussian-style standardisation an SVM expects.
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This MLA-C01 practice question is part of Courseiva's free Amazon Web Services 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 MLA-C01 exam.