MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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 analyzing a dataset with missing values in 30% of the rows for the 'age' column. The data scientist decides to impute the missing values with the median of the observed 'age' values. What is a potential drawback of this approach?
The imputation will introduce bias if the missing values are not random.
Why wrong: This is a potential drawback, but the question asks for a drawback of median imputation specifically; the more direct drawback is reduced variance.
B
Imputation using median is computationally expensive for large datasets.
Why wrong: Median imputation is computationally cheap.
C
The imputed values may reduce the variance of the 'age' distribution.
Replacing missing values with a constant reduces the variability of the feature.
D
The imputed values will increase the variance of the feature, leading to overfitting.
Why wrong: Median imputation does not increase variance.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The imputed values may reduce the variance of the 'age' distribution.
Imputing missing values with the median of the observed data artificially concentrates imputed values around the center of the distribution. This reduces the overall variance of the 'age' column because the imputed values do not reflect the natural spread of the data, potentially distorting downstream analyses like regression or clustering that rely on variance structure.
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.
✗
The imputation will introduce bias if the missing values are not random.
Why it's wrong here
This is a potential drawback, but the question asks for a drawback of median imputation specifically; the more direct drawback is reduced variance.
✗
Imputation using median is computationally expensive for large datasets.
Why it's wrong here
Median imputation is computationally cheap.
✓
The imputed values may reduce the variance of the 'age' distribution.
Why this is correct
Replacing missing values with a constant reduces the variability of the feature.
Related concept
Read the scenario before looking for a memorised answer.
✗
The imputed values will increase the variance of the feature, leading to overfitting.
Why it's wrong here
Median imputation does not increase variance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the subtle distinction between bias (which is a general risk of any imputation under non-random missingness) and variance reduction (which is a specific, guaranteed statistical consequence of constant-value imputation).
Detailed technical explanation
How to think about this question
Median imputation preserves the central tendency but compresses the distribution by replacing missing values with a constant, which shrinks the variance artificially. In practice, this can attenuate correlations with other features and reduce the power of statistical tests. A common mitigation is to use multiple imputation or model-based approaches that preserve the natural variability by adding random noise or drawing from conditional distributions.
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.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The imputed values may reduce the variance of the 'age' distribution. — Imputing missing values with the median of the observed data artificially concentrates imputed values around the center of the distribution. This reduces the overall variance of the 'age' column because the imputed values do not reflect the natural spread of the data, potentially distorting downstream analyses like regression or clustering that rely on variance structure.
What should I do if I get this MLS-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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Question Discussion
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