- A
Feature scaling
Why wrong: Feature scaling is for numerical features, not for encoding categoricals.
- B
Imputation of missing values
Why wrong: Imputation handles missing data, not encoding of categorical features.
- C
Principal component analysis (PCA)
Why wrong: PCA is dimensionality reduction, not an encoding method.
- D
One-hot encoding
One-hot encoding creates binary columns for each category, suitable for linear regression.
- E
Label encoding
Why wrong: Label encoding assigns arbitrary integers that imply order, which can mislead the model.
Question 189 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 company wants to build an ML model to predict customer lifetime value. The dataset includes numerical features (age, income) and categorical features (gender, region). Which TWO preprocessing steps should be applied to the categorical features before training a linear regression model? (Choose TWO.)
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
✓
One-hot encoding
One-hot encoding is required for linear regression because it converts categorical features into binary vectors without implying any ordinal relationship. Linear regression assumes numerical inputs, and one-hot encoding ensures that each category is treated as an independent predictor, avoiding the false ordering that label encoding would introduce.
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.
- ✗
Feature scaling
Why it's wrong here
Feature scaling is for numerical features, not for encoding categoricals.
- ✗
Imputation of missing values
Why it's wrong here
Imputation handles missing data, not encoding of categorical features.
- ✗
Principal component analysis (PCA)
Why it's wrong here
PCA is dimensionality reduction, not an encoding method.
- ✓
One-hot encoding
Why this is correct
One-hot encoding creates binary columns for each category, suitable for linear regression.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Label encoding
Why it's wrong here
Label encoding assigns arbitrary integers that imply order, which can mislead the model.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between label encoding and one-hot encoding, trapping candidates who think label encoding is acceptable for linear models when it actually introduces ordinal bias.
Detailed technical explanation
How to think about this question
One-hot encoding creates a binary column for each category, avoiding the implicit ordering problem. In linear regression, the coefficients for each dummy variable represent the expected change in the target when that category is present versus the reference category. A common subtlety is the dummy variable trap: to avoid perfect multicollinearity, one category must be dropped (reference category) when the model includes an intercept.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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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: One-hot encoding — One-hot encoding is required for linear regression because it converts categorical features into binary vectors without implying any ordinal relationship. Linear regression assumes numerical inputs, and one-hot encoding ensures that each category is treated as an independent predictor, avoiding the false ordering that label encoding would introduce.
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.
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
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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