- A
Normalize the target variable
Why wrong: Normalization of the target is not typically required for linear regression.
- B
Apply PCA to reduce dimensionality
Why wrong: PCA is optional and not a required preprocessing step.
- C
Remove all rows with missing values
Why wrong: Removing rows can lead to data loss; imputation is often preferred unless missingness is high.
- D
Encode categorical variables using one-hot encoding
Linear regression requires numerical input, so categorical variables must be encoded.
- E
Impute missing numerical values with the mean
Mean imputation is a common method for numerical missing values.
Question 29 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 data scientist is preparing a dataset for training a linear regression model. The dataset contains missing values and categorical features. Which TWO actions are appropriate to perform during data preprocessing? (Select 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
✓
Encode categorical variables using one-hot encoding
One-hot encoding is appropriate for converting categorical features into a numerical format that linear regression can process, as it creates binary columns for each category without implying ordinal relationships. This preserves the categorical information without introducing arbitrary numerical ordering that could bias 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.
- ✗
Normalize the target variable
Why it's wrong here
Normalization of the target is not typically required for linear regression.
- ✗
Apply PCA to reduce dimensionality
Why it's wrong here
PCA is optional and not a required preprocessing step.
- ✗
Remove all rows with missing values
Why it's wrong here
Removing rows can lead to data loss; imputation is often preferred unless missingness is high.
- ✓
Encode categorical variables using one-hot encoding
Why this is correct
Linear regression requires numerical input, so categorical variables must be encoded.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Impute missing numerical values with the mean
Why this is correct
Mean imputation is a common method for numerical missing values.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The AWS AI Practitioner exam often tests the distinction between feature preprocessing (scaling, encoding, imputation) and target variable manipulation, leading candidates to incorrectly select normalization of the target variable as a necessary step.
Detailed technical explanation
How to think about this question
One-hot encoding creates k binary columns for k categories, but to avoid multicollinearity (the dummy variable trap), one category is often dropped or regularization is used. Imputing missing numerical values with the mean is a simple, unbiased estimator under MCAR (Missing Completely At Random) but can reduce variance and distort relationships if missingness is systematic; more robust methods like median or model-based imputation are sometimes preferred.
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
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: Encode categorical variables using one-hot encoding — One-hot encoding is appropriate for converting categorical features into a numerical format that linear regression can process, as it creates binary columns for each category without implying ordinal relationships. This preserves the categorical information without introducing arbitrary numerical ordering that could bias the model.
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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