- A
Impute with mode
Why wrong: Mode is for categorical variables, not numerical.
- B
Impute with mean
Mean is appropriate for normally distributed numerical data without outliers.
- C
Impute with median
Why wrong: Median is robust to outliers, but not necessary here and may introduce bias.
- D
Drop rows with missing values
Why wrong: Dropping rows reduces dataset size and may bias the model.
Question 787 of 1,000
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 a linear regression model. The dataset has a few missing values in a numerical feature with a normal distribution and no outliers. Which imputation method is most appropriate?
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 with mean
For a numerical feature with a normal distribution and no outliers, the mean is the most appropriate imputation method because it preserves the central tendency of the data without introducing bias. In linear regression, mean imputation maintains the expected value of the feature, which is critical for unbiased coefficient estimates when data are missing completely at random (MCAR).
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.
- ✗
Impute with mode
Why it's wrong here
Mode is for categorical variables, not numerical.
- ✓
Impute with mean
Why this is correct
Mean is appropriate for normally distributed numerical data without outliers.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Impute with median
Why it's wrong here
Median is robust to outliers, but not necessary here and may introduce bias.
- ✗
Drop rows with missing values
Why it's wrong here
Dropping rows reduces dataset size and may bias the model.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse the median with the mean for normal distributions, but the median is actually less efficient and can lead to biased variance estimates, while the mean is the maximum likelihood estimator for normally distributed data with no outliers.
Detailed technical explanation
How to think about this question
Under the hood, mean imputation replaces missing values with the sample mean of the observed values, which minimizes the sum of squared errors for the imputed values and preserves the overall mean of the feature. However, this method artificially reduces variance and can attenuate correlations, so it is most appropriate when the missing rate is low (e.g., <5%) and the data are MCAR. In real-world scenarios, such as sensor data with occasional dropouts, mean imputation is often used as a quick baseline before more sophisticated methods like multiple imputation or model-based imputation.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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Data Preparation for Machine Learning — study guide chapter
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Impute with mean — For a numerical feature with a normal distribution and no outliers, the mean is the most appropriate imputation method because it preserves the central tendency of the data without introducing bias. In linear regression, mean imputation maintains the expected value of the feature, which is critical for unbiased coefficient estimates when data are missing completely at random (MCAR).
What should I do if I get this MLA-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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