- A
Drop all rows with any missing values
Why wrong: Dropping rows reduces data size; imputation is generally preferred unless missingness is excessive.
- B
Normalize all numerical features to [0,1] range
Why wrong: Normalization is not always required; tree-based models are scale-invariant.
- C
Impute missing square footage values with the median
Imputation with median is robust to outliers and preserves data.
- D
Apply label encoding to the neighborhood variable
Why wrong: Label encoding implies ordinality; neighborhood has no inherent order.
- E
Apply one-hot encoding to the neighborhood variable
One-hot encoding is appropriate for nominal categorical variables like neighborhood.
Question 129 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 building a regression model to predict house prices. During feature engineering, they have categorical variables (e.g., neighborhood) and numerical variables (e.g., square footage) with missing values. Which TWO actions should the data scientist take? (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
✓
Impute missing square footage values with the median
Option C is correct because imputing missing numerical values with the median is a robust technique that preserves the central tendency of the data without being influenced by outliers, which is especially important for features like square footage that may have skewed distributions. Option E is correct because one-hot encoding creates binary columns for each category of the neighborhood variable, which avoids imposing an arbitrary ordinal relationship that could mislead the regression model. Together, these actions handle missing data and categorical variables appropriately for a linear regression 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.
- ✗
Drop all rows with any missing values
Why it's wrong here
Dropping rows reduces data size; imputation is generally preferred unless missingness is excessive.
- ✗
Normalize all numerical features to [0,1] range
Why it's wrong here
Normalization is not always required; tree-based models are scale-invariant.
- ✓
Impute missing square footage values with the median
Why this is correct
Imputation with median is robust to outliers and preserves data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Apply label encoding to the neighborhood variable
Why it's wrong here
Label encoding implies ordinality; neighborhood has no inherent order.
- ✓
Apply one-hot encoding to the neighborhood variable
Why this is correct
One-hot encoding is appropriate for nominal categorical variables like neighborhood.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
In the AWS AI Practitioner exam, a common trap is that candidates may incorrectly choose label encoding (Option D) because it seems simpler, without recognizing that it introduces an arbitrary ordinal relationship that degrades model performance.
Detailed technical explanation
How to think about this question
Under the hood, one-hot encoding creates k-1 dummy variables (to avoid multicollinearity) when using a linear regression model with an intercept, ensuring that the design matrix has full rank. Imputing with the median is a form of univariate imputation that does not assume normality, making it safer than mean imputation for skewed distributions; however, it does not account for relationships between features, which more advanced methods like MICE or KNN imputation would capture. In a real-world scenario, a data scientist might also consider using a pipeline with a SimpleImputer and OneHotEncoder from scikit-learn to streamline preprocessing and avoid data leakage.
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: Impute missing square footage values with the median — Option C is correct because imputing missing numerical values with the median is a robust technique that preserves the central tendency of the data without being influenced by outliers, which is especially important for features like square footage that may have skewed distributions. Option E is correct because one-hot encoding creates binary columns for each category of the neighborhood variable, which avoids imposing an arbitrary ordinal relationship that could mislead the regression model. Together, these actions handle missing data and categorical variables appropriately for a linear regression 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
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