Quick Answer
The correct answer is the Handle Outliers transform, because it is the only built-in SageMaker Data Wrangler option specifically designed to detect and treat anomalous values using statistical methods like Interquartile Range (IQR) and z-score. For a feature like 'age' ranging from 0 to 120, these methods can flag improbable entries—such as ages above 100 or below 0—by comparing each value against the feature's distribution. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to match a data preparation task to the correct Data Wrangler transform, a common scenario in the Data Engineering section. A frequent trap is confusing "Handle Missing" (for nulls) or "Scale and Normalize" (for feature scaling) with outlier detection, but remember: outliers are about extreme values, not missing or scaled data. Memory tip: "Outliers need a Handle" — the transform name literally tells you its purpose.
This MLS-C01 practice question tests your understanding of exploratory data analysis. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 using Amazon SageMaker Data Wrangler to perform exploratory data analysis on a dataset. The dataset contains a feature 'age' with values ranging from 0 to 120. The data scientist wants to detect outliers. Which built-in transform in Data Wrangler is most appropriate for this task?
Handle Outliers
This transform includes outlier detection methods such as IQR and z-score.
Scale and Normalize
Why wrong: This transform standardizes or normalizes features but does not identify outliers.
Handle Missing
Why wrong: This transform addresses null values, not outliers.
Encode Categorical
Why wrong: This transform converts categorical variables to numeric, not for outlier detection.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Handle Outliers
Option C is correct because the 'Handle Outliers' transform provides methods like IQR and z-score to detect and handle outliers. Option A is wrong because 'Handle Missing' deals with missing values, not outliers. Option B is wrong because 'Scale and Normalize' transforms data but does not detect outliers. Option D is wrong because 'Encode Categorical' is for categorical features.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Handle Outliers
Why this is correct
This transform includes outlier detection methods such as IQR and z-score.
Related concept
Read the scenario before looking for a memorised answer.
Scale and Normalize
Why it's wrong here
This transform standardizes or normalizes features but does not identify outliers.
Handle Missing
Why it's wrong here
This transform addresses null values, not outliers.
Encode Categorical
Why it's wrong here
This transform converts categorical variables to numeric, not for outlier detection.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
The correct answer is: Handle Outliers — Option C is correct because the 'Handle Outliers' transform provides methods like IQR and z-score to detect and handle outliers. Option A is wrong because 'Handle Missing' deals with missing values, not outliers. Option B is wrong because 'Scale and Normalize' transforms data but does not detect outliers. Option D is wrong because 'Encode Categorical' is for categorical features.
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.
Read the scenario before looking for a memorised answer.
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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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