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Question 506 of 1,755

Exploratory Data Analysis →hardMultiple SelectObjective-mapped

Quick Answer

The correct answer is that understanding the missing data mechanism—whether MCAR, MAR, or MNAR—is critical for choosing an appropriate imputation strategy. This is because each mechanism implies a different relationship between the missingness and the data values: MCAR (Missing Completely at Random) means the missingness is unrelated to any data, MAR (Missing at Random) means it depends on observed data but not the missing values themselves, and MNAR (Missing Not at Random) means the missingness is related to the unobserved values. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your ability to avoid common pitfalls like using listwise deletion on non-MCAR data or mean imputation that reduces variance. A frequent trap is assuming mean imputation is always safe, but it distorts distributions under MAR or MNAR. Memory tip: MCAR is the only mechanism where you can safely delete rows without bias—think “MCAR = Missing Completely at Random = safe to drop.”

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.

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

Question 1hardmulti select

Full question →

A
Missing values can be ignored during EDA and handled during model training.
Why wrong: EDA should address missing data to inform preprocessing.
B
Visualizing the pattern of missingness can help determine if data is missing at random.
Missingness patterns inform assumptions about missing data mechanisms.
C
Understanding the missing data mechanism (MCAR, MAR, MNAR) is important for choosing an imputation strategy.
The mechanism affects the validity of imputation methods.
D
Listwise deletion (removing rows with missing values) is always safe and unbiased.
Why wrong: Listwise deletion can bias results if data is not MCAR.
E
Imputing missing values with the mean preserves the original variance.
Why wrong: Mean imputation reduces variance and can distort relationships.

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

✓

Visualizing the pattern of missingness can help determine if data is missing at random.

Options B and D are correct. B: Visualizing missing patterns (e.g., with a missingno matrix) is a good EDA practice. D: Understanding the mechanism (MCAR, MAR, MNAR) is critical for choosing imputation method. A is wrong because listwise deletion can introduce bias if data not MCAR. C is wrong because mean imputation reduces variance. E is wrong because missing values should be handled before model training, not after.

Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Missing values can be ignored during EDA and handled during model training.
Why it's wrong here
EDA should address missing data to inform preprocessing.
✓
Visualizing the pattern of missingness can help determine if data is missing at random.
Why this is correct
Missingness patterns inform assumptions about missing data mechanisms.
Related concept
Static NAT maps one inside address to one outside address.
✓
Understanding the missing data mechanism (MCAR, MAR, MNAR) is important for choosing an imputation strategy.
Why this is correct
The mechanism affects the validity of imputation methods.
Related concept
Static NAT maps one inside address to one outside address.
✗
Listwise deletion (removing rows with missing values) is always safe and unbiased.
Why it's wrong here
Listwise deletion can bias results if data is not MCAR.
✗
Imputing missing values with the mean preserves the original variance.
Why it's wrong here
Mean imputation reduces variance and can distort relationships.

Common exam traps

Common exam trap: NAT rules depend on direction and matching traffic

NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.

Detailed technical explanation

How to think about this question

NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.

KKey Concepts to Remember

Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.

TExam Day Tips

Identify inside and outside interfaces first.
Check whether the scenario needs static NAT, dynamic NAT or PAT.
Do not confuse NAT matching ACLs with normal packet-filtering intent.

Key takeaway

NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.

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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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.

Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.

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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 — Static NAT maps one inside address to one outside address..

What is the correct answer to this question?

The correct answer is: Visualizing the pattern of missingness can help determine if data is missing at random. — Options B and D are correct. B: Visualizing missing patterns (e.g., with a missingno matrix) is a good EDA practice. D: Understanding the mechanism (MCAR, MAR, MNAR) is critical for choosing imputation method. A is wrong because listwise deletion can introduce bias if data not MCAR. C is wrong because mean imputation reduces variance. E is wrong because missing values should be handled before model training, not after.

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

What is the key concept behind this question?

Static NAT maps one inside address to one outside address.

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 EDA are correct? (Select TWO.)

medium

A.Dropping columns with >50% missing values is always recommended.
B.Mean imputation preserves the variance of the original distribution.
✓ C.If data are missing completely at random (MCAR), listwise deletion yields unbiased estimates.
D.Multiple imputation (MICE) is always the safest method regardless of missing data mechanism.
✓ E.Imputing with the median is more robust to outliers than imputing with the mean.

Why C: Options B and C are correct. Option A is wrong because MICE is multivariate imputation, not necessarily safest. Option D is wrong because listwise deletion can introduce bias. Option E is wrong because mean imputation reduces variance.

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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.