This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. 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 of the following are types of feature scaling?
One-hot encoding
Why wrong: One-hot encoding is a categorical encoding technique.
Principal Component Analysis (PCA)
Why wrong: PCA is a dimensionality reduction technique, not a scaling method.
Standardization
Standardization (Z-score) is a common feature scaling method.
Binning
Why wrong: Binning is discretization, not scaling.
Normalization (Min-Max)
Min-Max scaling is another common feature scaling technique.
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
Standardization (Z-score scaling) transforms features to have a mean of 0 and a standard deviation of 1, making it a valid type of feature scaling. It is essential when using algorithms that assume normally distributed data, such as linear regression, SVM, or PCA, and it does not bound the data to a fixed range.
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 a categorical encoding technique.
Principal Component Analysis (PCA)
Why it's wrong here
PCA is a dimensionality reduction technique, not a scaling method.
Standardization
Why this is correct
Standardization (Z-score) is a common feature scaling method.
Related concept
Read the scenario before looking for a memorised answer.
Binning
Why it's wrong here
Binning is discretization, not scaling.
Normalization (Min-Max)
Why this is correct
Min-Max scaling is another common feature scaling technique.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
AWS often tests the distinction between feature scaling (changing the numeric range of features) and data transformation techniques like encoding or dimensionality reduction, leading candidates to confuse one-hot encoding or PCA with scaling methods.
Detailed technical explanation
Feature scaling adjusts the range or distribution of feature values to prevent variables with larger magnitudes from dominating distance-based algorithms like k-NN or gradient descent. Standardization uses the formula (x - μ) / σ, which is robust to outliers compared to Min-Max normalization, which rescales to a fixed range [0,1] using (x - min) / (max - min). In practice, scaling is critical for convergence speed in neural networks and for fair feature weighting in clustering algorithms.
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
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
The correct answer is: Standardization — Standardization (Z-score scaling) transforms features to have a mean of 0 and a standard deviation of 1, making it a valid type of feature scaling. It is essential when using algorithms that assume normally distributed data, such as linear regression, SVM, or PCA, and it does not bound the data to a fixed range.
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
This AIF-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 AIF-C01 exam.
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