- A
Standardization
Correct: Centers features to mean 0 and standard deviation 1.
- B
One-hot encoding
Why wrong: One-hot encoding converts categorical variables to binary columns.
- C
Min-Max scaling
Correct: Rescales features to a range [0,1].
- D
Normalization
Why wrong: Normalization is sometimes used interchangeably with scaling, but it is less precise than Min-Max.
- E
PCA
Why wrong: PCA is a dimensionality reduction technique, not scaling.
Question 489 of 1,000
Feature Scaling Techniques: Standardization and Min-Max Scaling
This AI0-001 practice question tests your understanding of machine learning and deep learning. 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 commonly used for feature scaling? (Choose two.)
Quick Answer
The answer is Min-Max scaling and standardization. These two techniques are the most commonly used methods for feature scaling because they transform numerical data into a consistent range, ensuring that no single feature dominates a machine learning model due to its scale. Standardization, also known as Z-score normalization, rescales data to have a mean of zero and a standard deviation of one, making it ideal for algorithms that assume normally distributed data. Min-Max scaling, on the other hand, shrinks values to a fixed range, typically 0 to 1, which is particularly useful for neural networks and distance-based algorithms like k-nearest neighbors. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of data preprocessing fundamentals, often appearing alongside traps like confusing PCA (a dimensionality reduction technique) or one-hot encoding (for categorical data) with scaling. A common memory tip: think of standardization as “centering and spreading” and Min-Max as “squeezing into a box”—both are scaling, not transformation or encoding.
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
✓
Standardization
Standardization (A) is correct because it rescales features to have a mean of 0 and a standard deviation of 1, which is essential for algorithms like SVM, PCA, and neural networks that assume normally distributed data. This technique uses the formula (x - μ) / σ, where μ is the mean and σ is the standard deviation, ensuring each feature contributes equally to the model.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Standardization
Why this is correct
Correct: Centers features to mean 0 and standard deviation 1.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
One-hot encoding
Why it's wrong here
One-hot encoding converts categorical variables to binary columns.
- ✓
Min-Max scaling
Why this is correct
Correct: Rescales features to a range [0,1].
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Normalization
Why it's wrong here
Normalization is sometimes used interchangeably with scaling, but it is less precise than Min-Max.
- ✗
PCA
Why it's wrong here
PCA is a dimensionality reduction technique, not scaling.
Common exam traps
Common exam trap: answer the scenario, not the keyword
This question tests the distinction between 'Normalization' (which can refer to scaling to unit length) and 'Min-Max scaling' (which rescales to a fixed range), leading candidates to incorrectly select Normalization as a feature scaling technique.
Detailed technical explanation
How to think about this question
Under the hood, standardization assumes data follows a Gaussian distribution and is robust to outliers because it uses mean and standard deviation, whereas Min-Max scaling (C) compresses values into a fixed range [0,1] using (x - min) / (max - min), making it sensitive to outliers. In real-world scenarios, using standardization for neural networks helps gradient descent converge faster, while Min-Max scaling is preferred for algorithms like k-NN that rely on distance metrics.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Standardization — Standardization (A) is correct because it rescales features to have a mean of 0 and a standard deviation of 1, which is essential for algorithms like SVM, PCA, and neural networks that assume normally distributed data. This technique uses the formula (x - μ) / σ, where μ is the mean and σ is the standard deviation, ensuring each feature contributes equally to the model.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
This AI0-001 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 AI0-001 exam.
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