Quick Answer
The answer is Min-Max scaling and Standardization (Z-score normalization). These two feature scaling techniques are correct because they transform data to a common scale without distorting differences in the ranges of values, which is critical for distance-based and gradient-based algorithms. Min-Max scaling rescales features to a fixed range, typically [0, 1], using the formula (x - min) / (max - min), while Standardization centers data around a mean of 0 with a standard deviation of 1 using z = (x - μ) / σ. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of preprocessing steps that affect model convergence and performance, especially for algorithms like SVM, k-means, and PCA that assume normally distributed features and are sensitive to feature magnitudes. A common trap is confusing normalization with standardization or assuming only one technique exists. Memory tip: think “Min-Max for bounded ranges, Standardization for bell curves.”
This MLS-C01 practice question tests your understanding of modeling. 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.
Which TWO techniques are used for feature scaling? (Choose 2.)
One-hot encoding
Why wrong: One-hot encoding is for categorical variables, not scaling.
Standardization (Z-score normalization)
Standardization scales features to have mean 0 and variance 1.
Min-Max scaling
Min-Max scaling scales features to a fixed range, usually [0,1].
Principal Component Analysis (PCA)
Why wrong: PCA is dimensionality reduction, not scaling.
Label encoding
Why wrong: Label encoding is for ordinal categorical variables.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Standardization (Z-score normalization)
Standardization (Z-score normalization) is a feature scaling technique that transforms data to have a mean of 0 and a standard deviation of 1, using the formula z = (x - μ) / σ. This is essential for algorithms like SVM, k-means, and PCA that assume normally distributed features and are sensitive to feature magnitudes.
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.
One-hot encoding
Why it's wrong here
One-hot encoding is for categorical variables, not scaling.
Standardization (Z-score normalization)
Why this is correct
Standardization scales features to have mean 0 and variance 1.
Related concept
Read the scenario before looking for a memorised answer.
Min-Max scaling
Why this is correct
Min-Max scaling scales features to a fixed range, usually [0,1].
Related concept
Read the scenario before looking for a memorised answer.
Principal Component Analysis (PCA)
Why it's wrong here
PCA is dimensionality reduction, not scaling.
Label encoding
Why it's wrong here
Label encoding is for ordinal categorical variables.
Common exam traps
Cisco often tests the distinction between feature scaling techniques (which transform numerical feature values) and encoding or dimensionality reduction techniques, leading candidates to mistakenly select one-hot encoding or PCA as scaling methods.
Detailed technical explanation
Standardization is robust to outliers because it uses mean and standard deviation, while Min-Max scaling is sensitive to outliers as it uses min and max values. In practice, standardization is preferred for algorithms that assume Gaussian distributions, whereas Min-Max scaling is ideal for neural networks where bounded inputs (e.g., [0,1]) improve convergence. A subtle behavior: standardization does not guarantee a fixed range, which can cause issues with sparse data or when using distance-based metrics.
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
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Standardization (Z-score normalization) — Standardization (Z-score normalization) is a feature scaling technique that transforms data to have a mean of 0 and a standard deviation of 1, using the formula z = (x - μ) / σ. This is essential for algorithms like SVM, k-means, and PCA that assume normally distributed features and are sensitive to feature magnitudes.
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 24, 2026
This MLS-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 MLS-C01 exam.
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