- A
Remove rows with any missing values
Why wrong: Deleting rows reduces sample size and may bias data if missingness is not random.
- B
Use iterative imputation (MICE) to model missing values
MICE uses relationships among variables to impute, reducing bias.
- C
Replace missing values with the mode of each column
Why wrong: Mode imputation can introduce bias for categorical variables.
- D
Replace missing values with the mean of each column
Why wrong: Mean imputation can distort distributions and correlations.
Question 14 of 1,755
MLS-C01 MICE 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. A key principle to apply: mICE. 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 has a dataset with a large number of missing values in several columns. The data scientist wants to impute missing values without introducing bias. Which approach should be used?
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
✓
Use iterative imputation (MICE) to model missing values
Option B is correct because Multiple Imputation by Chained Equations (MICE) models each variable with missing values as a function of other variables, reducing bias compared to simpler methods. Option A is wrong because removing rows with missing values leads to data loss and potential bias if missingness is not random. Option C is wrong because replacing with the mode ignores relationships between variables and can introduce bias. Option D is wrong because mean imputation reduces variance and can distort relationships.
Key principle: MICE
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove rows with any missing values
Why it's wrong here
Deleting rows reduces sample size and may bias data if missingness is not random.
- ✓
Use iterative imputation (MICE) to model missing values
Why this is correct
MICE uses relationships among variables to impute, reducing bias.
Related concept
MICE
- ✗
Replace missing values with the mode of each column
Why it's wrong here
Mode imputation can introduce bias for categorical variables.
- ✗
Replace missing values with the mean of each column
Why it's wrong here
Mean imputation can distort distributions and correlations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The correct answer is B (MICE). A common mistake is to assume mean/mode imputation is acceptable, but MICE provides less biased imputations by leveraging correlations among features.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- MICE
- Missingness mechanisms
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
MICE
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. MICE 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.
Review mICE, then practise related MLS-C01 questions on the same topic to reinforce the concept.
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Exploratory Data Analysis — study guide chapter
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Exploratory Data Analysis — This question tests Exploratory Data Analysis — MICE.
What is the correct answer to this question?
The correct answer is: Use iterative imputation (MICE) to model missing values — Option B is correct because Multiple Imputation by Chained Equations (MICE) models each variable with missing values as a function of other variables, reducing bias compared to simpler methods. Option A is wrong because removing rows with missing values leads to data loss and potential bias if missingness is not random. Option C is wrong because replacing with the mode ignores relationships between variables and can introduce bias. Option D is wrong because mean imputation reduces variance and can distort relationships.
What should I do if I get this MLS-C01 question wrong?
Review mICE, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
MICE
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Last reviewed: Jun 20, 2026
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