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.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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 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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
Option B is correct because Multiple Imputation by Chained Equations (MICE) accounts for relationships between features and preserves variability. Option A is wrong because mean imputation can bias estimates when data is not missing completely at random. Option C is wrong because dropping rows reduces sample size and may introduce bias. Option D is wrong because last observation carried forward is for time series.
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.
✗
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.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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: Multiple imputation using MICE — Option B is correct because Multiple Imputation by Chained Equations (MICE) accounts for relationships between features and preserves variability. Option A is wrong because mean imputation can bias estimates when data is not missing completely at random. Option C is wrong because dropping rows reduces sample size and may introduce bias. Option D is wrong because last observation carried forward is for time series.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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: Option D is correct because multiple imputation by chained equations (MICE) handles MAR well by modeling each variable with missing values conditional on others. Option A is wrong because mean imputation underestimates variance. Option B is wrong because dropping rows with missing data reduces sample size and can introduce bias. Option C is wrong because KNN imputation assumes data are MCAR and may not be optimal for MAR.
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: Option C is correct because mean imputation can introduce bias when data is not missing completely at random, as it reduces variance and distorts relationships. Option A (multiple imputation) and B (model-based imputation) are appropriate for non-random missing data. Option D (k-NN imputation) can also be used but may be less biased than mean imputation.
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.
Last reviewed: Jun 20, 2026
Question Discussion
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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.
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