The correct answer is to use multiple imputation to generate several plausible values and combine the results. This approach is essential for missing at random (MAR) data because it accounts for the uncertainty inherent in the missing values by creating multiple complete datasets, analyzing each separately, and pooling the estimates—yielding unbiased parameter estimates and proper standard errors. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of how imputation strategies affect bias and variance, often appearing in scenario-based questions where a single imputation method like mean or median is a tempting but flawed shortcut. A common trap is choosing mean imputation, which artificially reduces variance and distorts relationships with other variables, or dropping rows, which can introduce bias if missingness correlates with observed data. Remember the memory tip: “MAR means multiple—never settle for one.”
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 engineer is performing exploratory data analysis on a dataset stored in Amazon S3 using AWS Glue DataBrew. The dataset contains a column 'age' with missing values. DataBrew's profile shows that the column has 5% missing values, a mean of 45, and a standard deviation of 15. Which imputation strategy should the engineer recommend to minimize bias if the missing data is Missing at Random (MAR)?
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
✓
Use multiple imputation to generate several plausible values and combine results
Option C is correct because multiple imputation provides unbiased estimates under MAR by accounting for uncertainty. Option A is wrong because mean imputation reduces variance and can bias relationships. Option B is wrong because median imputation is robust but still single imputation. Option D is wrong because dropping rows reduces sample size and may introduce bias if missingness is related to other variables.
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.
✗
Replace missing values with the mean (45)
Why it's wrong here
Mean imputation distorts the distribution and can bias correlations.
✗
Remove rows with missing 'age' values
Why it's wrong here
Listwise deletion can introduce bias if the missingness is related to other variables.
✗
Replace missing values with the median
Why it's wrong here
Median imputation is robust but still a single imputation method that underestimates variability.
✓
Use multiple imputation to generate several plausible values and combine results
Why this is correct
Multiple imputation preserves the natural variability and provides valid statistical inferences under MAR.
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.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: Use multiple imputation to generate several plausible values and combine results — Option C is correct because multiple imputation provides unbiased estimates under MAR by accounting for uncertainty. Option A is wrong because mean imputation reduces variance and can bias relationships. Option B is wrong because median imputation is robust but still single imputation. Option D is wrong because dropping rows reduces sample size and may introduce bias if missingness is related to other variables.
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.
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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.
Question Discussion
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