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Question 995 of 1,755

Exploratory Data Analysis →mediumMultiple SelectObjective-mapped

Missing Data Mechanisms (MCAR) and Imputation — Robust Handling for EDA | AWS Machine Learning Specialty Explained

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.

Which TWO statements about handling missing data during EDA are correct? (Select TWO.)

A
Dropping columns with >50% missing values is always recommended.
Why wrong: Sometimes columns with high missingness still contain useful information.
B
Mean imputation preserves the variance of the original distribution.
Why wrong: Mean imputation reduces variance.
C
If data are missing completely at random (MCAR), listwise deletion yields unbiased estimates.
Under MCAR, missingness is independent of data, so deletion is unbiased.
D
Multiple imputation (MICE) is always the safest method regardless of missing data mechanism.
Why wrong: MICE is robust but not always safest; depends on MCAR assumption.
E
Imputing with the median is more robust to outliers than imputing with the mean.
Median is less affected by outliers.

Full breakdown with real-world context →

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

✓

If data are missing completely at random (MCAR), listwise deletion yields unbiased estimates.

Options C and E are correct. Option C is correct because when data are Missing Completely at Random (MCAR), the missingness is independent of both observed and unobserved data, so listwise deletion (removing rows with missing values) does not introduce bias; the remaining sample is still a random subsample. Option E is correct because the median is not influenced by extreme values, making it a more robust imputation method compared to the mean, which can be skewed by outliers. Option A is incorrect because dropping columns with >50% missing values is not always recommended; it depends on the importance of the variable and the analysis goals. Option B is incorrect because mean imputation reduces the variance of the imputed variable, as it forces imputed values to the center. Option D is incorrect because Multiple Imputation by Chained Equations (MICE) is not always the safest; it assumes data are Missing at Random (MAR) and can be complex or inappropriate for other missingness mechanisms.

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.

✗
Dropping columns with >50% missing values is always recommended.
Why it's wrong here
Sometimes columns with high missingness still contain useful information.
✗
Mean imputation preserves the variance of the original distribution.
Why it's wrong here
Mean imputation reduces variance.
✓
If data are missing completely at random (MCAR), listwise deletion yields unbiased estimates.
Why this is correct
Under MCAR, missingness is independent of data, so deletion is unbiased.
Related concept
Read the scenario before looking for a memorised answer.
✗
Multiple imputation (MICE) is always the safest method regardless of missing data mechanism.
Why it's wrong here
MICE is robust but not always safest; depends on MCAR assumption.
✓
Imputing with the median is more robust to outliers than imputing with the mean.
Why this is correct
Median is less affected by outliers.
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 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.

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FAQ

Questions learners often ask

What does this MLS-C01 question test?

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: If data are missing completely at random (MCAR), listwise deletion yields unbiased estimates. — Options C and E are correct. Option C is correct because when data are Missing Completely at Random (MCAR), the missingness is independent of both observed and unobserved data, so listwise deletion (removing rows with missing values) does not introduce bias; the remaining sample is still a random subsample. Option E is correct because the median is not influenced by extreme values, making it a more robust imputation method compared to the mean, which can be skewed by outliers. Option A is incorrect because dropping columns with >50% missing values is not always recommended; it depends on the importance of the variable and the analysis goals. Option B is incorrect because mean imputation reduces the variance of the imputed variable, as it forces imputed values to the center. Option D is incorrect because Multiple Imputation by Chained Equations (MICE) is not always the safest; it assumes data are Missing at Random (MAR) and can be complex or inappropriate for other missingness mechanisms.

What should I do if I get this MLS-C01 question wrong?

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

About these practice questions

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Same concept, more angles

1 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. Which TWO statements about handling missing data during exploratory data analysis are correct? (Select TWO.)

hard

A.Missing values can be ignored during EDA and handled during model training.
✓ B.Visualizing the pattern of missingness can help determine if data is missing at random.
✓ C.Understanding the missing data mechanism (MCAR, MAR, MNAR) is important for choosing an imputation strategy.
D.Listwise deletion (removing rows with missing values) is always safe and unbiased.
E.Imputing missing values with the mean preserves the original variance.

Why B: Options B and C are correct. Visualizing the pattern of missingness helps determine if data is missing at random, which is a key EDA step. Understanding the missing data mechanism (MCAR, MAR, MNAR) is important for selecting an appropriate imputation strategy. Option A is incorrect because missing values should be addressed during EDA, not deferred to model training. Option D is incorrect because listwise deletion can introduce bias if data is not MCAR. Option E is incorrect because mean imputation reduces the variance of the imputed variable.

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