Minimize Bias with MICE Imputation
A data scientist is analyzing a dataset with missing values. The missing data is not random and is correlated with other features. Which imputation method is most appropriate to minimize bias?
Quick Answer
The correct answer is Multiple Imputation by Chained Equations (MICE). This method is the most appropriate imputation technique to minimize bias under non-random missingness because it models each feature with missing values as a function of the other features, iteratively predicting and updating values to preserve the underlying relationships and variability in the data. Unlike simpler methods, MICE accounts for the correlation between the missing data and other features, which is critical when the missingness is not random and ignoring that structure would systematically distort estimates. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how missing data mechanisms—specifically Missing Not At Random (MNAR)—affect model bias, and it often appears alongside traps like mean imputation or dropping rows, which fail under these conditions. A common memory tip is to think of MICE as “chaining” the relationships: it uses the full feature set to fill gaps, not just a single average, making it robust when missingness is informative.
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
✓
Multiple imputation using MICE
Multiple Imputation by Chained Equations (MICE) accounts for relationships between features and preserves variability. Option A is wrong because last observation carried forward is only appropriate for time series data where missing values are filled with the previous observation; it does not handle non-random missing data correlated with other features. Option C is wrong because listwise deletion reduces sample size and may introduce bias when data is not missing completely at random. Option D is wrong because mean imputation can bias estimates and reduce variability, especially when missingness is related to other features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Last observation carried forward
Why it's wrong here
Incorrect: This method is appropriate for time series, not general tabular data.
- ✓
Multiple imputation using MICE
Why this is correct
Correct: MICE models missing values using other features, suitable for non-random missingness.
- ✗
Listwise deletion
Why it's wrong here
Incorrect: Deleting rows discards information and can introduce bias if missingness is not random.
- ✗
Mean imputation
Why it's wrong here
Incorrect: Mean imputation can distort relationships and bias results.
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Same concept, more angles
3 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is analyzing a dataset with missing values. The missing data mechanism is missing at random (MAR). Which imputation method is most appropriate to preserve relationships between variables?
hard- A.Remove all rows with any missing values.
- B.Use k-nearest neighbors imputation.
- ✓ C.Use multiple imputation by chained equations (MICE).
- D.Replace missing values with the mean of the column.
Why C: Multiple imputation by chained equations (MICE) is well-suited for missing at random (MAR) data as it models each variable with missing values conditional on other variables, preserving relationships. Option A (removing rows) reduces sample size and can introduce bias if data are not MCAR. Option B (KNN) assumes data are missing completely at random (MCAR) and may not handle MAR well. Option D (mean imputation) reduces variance and distorts relationships.
Variation 2. A data scientist is analyzing a dataset with missing values in a numeric column. The missing rate is 30% and the data is not missing completely at random. Which imputation method should the data scientist avoid to minimize bias?
medium- ✓ A.Mean imputation
- B.Model-based imputation using linear regression
- C.k-Nearest Neighbors imputation
- D.Multiple imputation using chained equations
Why A: Mean imputation (Option A) should be avoided when data is not missing completely at random (NMAR) because it can introduce bias by underestimating variance and distorting the relationships between variables. Options B (model-based imputation), C (k-NN imputation), and D (multiple imputation) are more robust for non-random missing data as they account for patterns in the data and produce less biased estimates.
Variation 3. 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?
medium- A.The imputation will introduce bias if the missing values are not random.
- B.Imputation using median is computationally expensive for large datasets.
- ✓ C.The imputed values may reduce the variance of the 'age' distribution.
- D.The imputed values will increase the variance of the feature, leading to overfitting.
Why C: 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.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-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 MLS-C01 exam.