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 performing exploratory data analysis on a dataset with 10,000 rows and 20 features. The target variable is binary. The data scientist observes that one feature has 15% missing values. Which TWO actions are appropriate to handle this missing data? (Choose TWO.)
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 fill in the missing values.
Option C is correct because multiple imputation is a robust statistical technique that accounts for uncertainty in missing values by creating multiple complete datasets, analyzing each, and pooling results. This is particularly appropriate for a dataset with 10,000 rows and 20 features, as it preserves the sample size and avoids bias that simpler methods might introduce.
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 mode of the feature.
Why it's wrong here
Mode imputation is simplistic and may introduce bias.
✗
Identify and remove outliers from the feature.
Why it's wrong here
Outlier removal does not address missing values.
✓
Use multiple imputation to fill in the missing values.
Why this is correct
Multiple imputation creates several plausible imputed datasets and combines results.
Related concept
Read the scenario before looking for a memorised answer.
✓
Delete all rows that contain missing values for this feature.
Why this is correct
If missingness is random and 15% is acceptable, listwise deletion is straightforward.
Related concept
Read the scenario before looking for a memorised answer.
✗
Drop the entire feature from the dataset.
Why it's wrong here
Dropping a feature with only 15% missing may discard valuable information.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that mode imputation (Option A) is a safe default for missing data, but it ignores feature relationships and can distort distributions, whereas multiple imputation is preferred for non-trivial missingness.
Detailed technical explanation
How to think about this question
Multiple imputation works by generating m complete datasets (typically m=5-20) using a model like MCMC or regression, where each imputed value includes random noise to reflect uncertainty. The results are combined using Rubin's rules, which adjust standard errors to account for the variability between imputations, providing valid statistical inference. In contrast, deleting rows with missing values (Option D) is acceptable only if the missingness is completely at random (MCAR) and the remaining sample size is still adequate for modeling.
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: Use multiple imputation to fill in the missing values. — Option C is correct because multiple imputation is a robust statistical technique that accounts for uncertainty in missing values by creating multiple complete datasets, analyzing each, and pooling results. This is particularly appropriate for a dataset with 10,000 rows and 20 features, as it preserves the sample size and avoids bias that simpler methods might introduce.
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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