Quick Answer
The answer is to create a new category labeled 'Missing' for the missing values. This approach is correct because it preserves the missingness pattern as a distinct signal within the data, avoiding the data loss of dropping rows or the bias introduced by imputing a category that may not represent the true distribution. For the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding that categorical features with missing values should not be treated like numerical features; mean imputation is invalid for categories, and mode imputation assumes missingness is random, which can skew the model. A common trap is defaulting to dropping rows or using mode imputation, but the exam emphasizes that missingness itself can be informative. Memory tip: think "Missing as a category" — for categorical features, treat missingness as its own level, not a problem to fix.
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.
During exploratory data analysis, a machine learning engineer finds that a dataset has a significant number of missing values in a categorical feature with 10 levels. Which approach should they take to handle these missing values before modeling?
Impute missing values with the mean of the feature.
Why wrong: Mean is not meaningful for categorical data.
Create a new category labeled 'Missing' for missing values.
Preserves the missingness pattern and avoids bias.
Drop all rows with missing values.
Why wrong: May discard significant data.
Impute missing values with the mode of the feature.
Why wrong: Mode imputation can introduce bias if missingness is non-random.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Create a new category labeled 'Missing' for missing values.
Option C is correct because creating a separate 'Missing' category preserves the missingness pattern and avoids data loss or bias from imputation for categorical features. Option A is incorrect because dropping rows with missing values may discard valuable data. Option B is incorrect because mean imputation is for numerical features, not categorical. Option D is incorrect because mode imputation may introduce bias if missingness is not random.
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
For each option: why learners choose it and why it is or isn't the right answer here.
Impute missing values with the mean of the feature.
Why it's wrong here
Mean is not meaningful for categorical data.
Create a new category labeled 'Missing' for missing values.
Why this is correct
Preserves the missingness pattern and avoids bias.
Related concept
Static NAT maps one inside address to one outside address.
Drop all rows with missing values.
Why it's wrong here
May discard significant data.
Impute missing values with the mode of the feature.
Why it's wrong here
Mode imputation can introduce bias if missingness is non-random.
Common exam traps
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
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.
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
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
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.
Exploratory Data Analysis — study guide chapter
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FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Static NAT maps one inside address to one outside address..
The correct answer is: Create a new category labeled 'Missing' for missing values. — Option C is correct because creating a separate 'Missing' category preserves the missingness pattern and avoids data loss or bias from imputation for categorical features. Option A is incorrect because dropping rows with missing values may discard valuable data. Option B is incorrect because mean imputation is for numerical features, not categorical. Option D is incorrect because mode imputation may introduce bias if missingness is not random.
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.
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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