- A
It eliminates the need for a bias term
Why wrong: The bias term is still needed to capture the intercept.
- B
It converts categorical variables into numerical values
Why wrong: That is encoding, not normalization.
- C
It ensures all features are on the same scale so that no feature dominates the model training
Standardization equalizes scales, improving model performance and convergence.
- D
It reduces the number of features
Why wrong: Normalization does not reduce feature count.
Question 340 of 1,000
AIF-C01 AI and ML Fundamentals Practice Question
This AIF-C01 practice question tests your understanding of ai and ml fundamentals. 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.
A machine learning engineer normalizes numerical features to have mean 0 and standard deviation 1 before training a linear regression model. What is the PRIMARY benefit of this normalization?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"primary"Why it matters: Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.
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
✓
It ensures all features are on the same scale so that no feature dominates the model training
Normalizing numerical features to have mean 0 and standard deviation 1 (z-score normalization) ensures all features are on the same scale. In linear regression, features with larger magnitudes can disproportionately influence the gradient descent updates and the learned coefficients, leading to unstable training or suboptimal convergence. By standardizing, each feature contributes equally to the model, improving both training stability and interpretability of the coefficients.
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.
- ✗
It eliminates the need for a bias term
Why it's wrong here
The bias term is still needed to capture the intercept.
- ✗
It converts categorical variables into numerical values
Why it's wrong here
That is encoding, not normalization.
- ✓
It ensures all features are on the same scale so that no feature dominates the model training
Why this is correct
Standardization equalizes scales, improving model performance and convergence.
Clue confirmation
The clue word "primary" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
It reduces the number of features
Why it's wrong here
Normalization does not reduce feature count.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that normalization removes the need for a bias term or that it reduces feature count, but the core purpose is purely to scale features so that no single feature dominates the training process due to its magnitude.
Detailed technical explanation
How to think about this question
Under the hood, z-score normalization subtracts the mean and divides by the standard deviation for each feature, which centers the data around zero and scales it to unit variance. This is particularly important for linear regression when using gradient descent, as it prevents features with larger ranges from dominating the step sizes and causing oscillations. In real-world scenarios, such as predicting house prices where square footage (thousands) and number of bedrooms (single digits) are used together, normalization ensures the model learns balanced coefficients and converges faster.
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
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.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
AI and ML Fundamentals — This question tests AI and ML Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: It ensures all features are on the same scale so that no feature dominates the model training — Normalizing numerical features to have mean 0 and standard deviation 1 (z-score normalization) ensures all features are on the same scale. In linear regression, features with larger magnitudes can disproportionately influence the gradient descent updates and the learned coefficients, leading to unstable training or suboptimal convergence. By standardizing, each feature contributes equally to the model, improving both training stability and interpretability of the coefficients.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "primary". Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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