The answer is the distribution of the feature, the missing data mechanism, and the proportion of missing values. These three considerations are critical because the choice of imputation method directly depends on whether the data is MCAR, MAR, or MNAR, as well as how much data is missing and whether the feature is skewed or contains outliers—for instance, mean imputation is inappropriate for a skewed distribution, while median or model-based imputation may be better. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between data preprocessing tactics and modeling concerns; a common trap is selecting “feature importance” or “class imbalance,” which are irrelevant to imputation strategy. Remember the mnemonic “MAD” for Missing mechanism, Amount missing, and Distribution shape to recall the three pillars of imputation selection.
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 analyzing a dataset with many missing values. The scientist wants to decide on an imputation strategy. Which THREE considerations are important for choosing the imputation method?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The mechanism of missingness (MCAR, MAR, MNAR).
Missing data mechanism (MCAR/MAR/MNAR), proportion of missing values, and feature distribution (skewness, outliers) all affect imputation choice. Option A (feature importance) is not directly relevant. Option D (class imbalance) is for classification targets.
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.
✓
The mechanism of missingness (MCAR, MAR, MNAR).
Why this is correct
Determines whether imputation is valid.
Related concept
Read the scenario before looking for a memorised answer.
✗
The class imbalance of the target variable.
Why it's wrong here
Class imbalance is a separate issue.
✓
The percentage of missing values in each feature.
Why this is correct
High missingness may require different strategies.
Related concept
Read the scenario before looking for a memorised answer.
✓
The distribution of the feature (e.g., skewed, normal).
Why this is correct
Mean imputation is inappropriate for skewed data.
Related concept
Read the scenario before looking for a memorised answer.
✗
The feature importance according to a random forest model.
Why it's wrong here
Feature importance does not guide imputation method.
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: The mechanism of missingness (MCAR, MAR, MNAR). — Missing data mechanism (MCAR/MAR/MNAR), proportion of missing values, and feature distribution (skewness, outliers) all affect imputation choice. Option A (feature importance) is not directly relevant. Option D (class imbalance) is for classification targets.
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.
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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