- A
Impute missing age values with the median and cap outliers in 'amount' using the interquartile range (IQR) method.
Median is robust; IQR handles outliers.
- B
Remove rows with missing age and apply log transformation to 'amount'.
Why wrong: Removing rows may reduce data.
- C
Impute missing age values with a constant (e.g., 0) and cap outliers using mean ± 3*std.
Why wrong: Constant imputation can bias results; mean-based capping is not robust.
- D
Impute missing age values with the mean and remove outliers in 'amount' using z-score.
Why wrong: Mean is sensitive to outliers.
Quick Answer
The correct answer is to impute missing age values with the median and cap outliers in the amount column using the interquartile range (IQR) method. This combination is ideal because median imputation is robust to outliers—unlike the mean, it won’t be skewed by extreme values—while IQR-based capping (typically setting values beyond 1.5 times the IQR to the nearest fence) directly addresses outliers without discarding data. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of robust preprocessing techniques during exploratory data analysis, a common scenario where you must handle both missing values and outliers without distorting the dataset’s distribution. A frequent trap is choosing mean imputation or z-score methods, which are sensitive to the very outliers you’re trying to manage. Memory tip: think “Median for missing, IQR for extreme”—both resist distortion, keeping your EDA clean.
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 machine learning engineer is performing exploratory data analysis on a dataset containing customer transaction records. The dataset has missing values in the 'age' column and outliers in the 'amount' column. Which combination of techniques should the engineer use to handle these issues during EDA?
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
Impute missing age values with the median and cap outliers in 'amount' using the interquartile range (IQR) method.
Option A is correct because median imputation is robust to outliers, and IQR-based capping is standard for outlier handling. Option B is wrong because mean imputation is sensitive to outliers. Option C is wrong because removing rows with missing age may lose data. Option D is wrong because z-score with mean/std is also sensitive to outliers.
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.
- ✓
Impute missing age values with the median and cap outliers in 'amount' using the interquartile range (IQR) method.
Why this is correct
Median is robust; IQR handles outliers.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
Remove rows with missing age and apply log transformation to 'amount'.
Why it's wrong here
Removing rows may reduce data.
- ✗
Impute missing age values with a constant (e.g., 0) and cap outliers using mean ± 3*std.
Why it's wrong here
Constant imputation can bias results; mean-based capping is not robust.
- ✗
Impute missing age values with the mean and remove outliers in 'amount' using z-score.
Why it's wrong here
Mean is sensitive to outliers.
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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Exploratory Data Analysis — study guide chapter
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Exploratory Data Analysis practice questions
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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: Impute missing age values with the median and cap outliers in 'amount' using the interquartile range (IQR) method. — Option A is correct because median imputation is robust to outliers, and IQR-based capping is standard for outlier handling. Option B is wrong because mean imputation is sensitive to outliers. Option C is wrong because removing rows with missing age may lose data. Option D is wrong because z-score with mean/std is also sensitive to outliers.
What should I do if I get this MLS-C01 question wrong?
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.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 20, 2026
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.
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