Quick Answer
The answer is Isolation Forest, as it is the most robust method for multivariate outlier detection when features follow different distributions. This algorithm works by recursively partitioning the dataset with random splits, isolating anomalies in fewer steps because outliers are rare and distinct, making it highly effective in high-dimensional spaces without any assumption about underlying data distributions. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how different outlier detection methods handle multivariate data and distributional assumptions—a common trap is choosing Z-score or IQR, which fail with multiple features or non-normal distributions. Remember that Isolation Forest is like a game of "20 Questions" for outliers: the odd ones out are found fastest, while Z-score and IQR are one-dimensional tools that miss the bigger picture.
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 data scientist needs to detect outliers in a dataset with multiple features that follow different distributions. Which method is most robust for multivariate outlier detection?
Z-score threshold
Why wrong: Incorrect: Z-score assumes normality and is univariate.
Interquartile range (IQR)
Why wrong: Incorrect: IQR is univariate and ignores feature interactions.
DBSCAN clustering
Why wrong: Incorrect: DBSCAN is primarily for clustering, not outlier detection, though it can identify noise points.
Isolation Forest
Correct: Isolation Forest works well for multivariate data without distributional assumptions.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Isolation Forest
Option C is correct because Isolation Forest is an ensemble method that isolates anomalies effectively in high-dimensional spaces without assuming distribution. Option A is wrong because Z-score assumes normal distribution. Option B is wrong because IQR is univariate. Option D is wrong because DBSCAN is for clustering, not specifically for outlier detection.
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.
Z-score threshold
Why it's wrong here
Incorrect: Z-score assumes normality and is univariate.
Interquartile range (IQR)
Why it's wrong here
Incorrect: IQR is univariate and ignores feature interactions.
DBSCAN clustering
Why it's wrong here
Incorrect: DBSCAN is primarily for clustering, not outlier detection, though it can identify noise points.
Isolation Forest
Why this is correct
Correct: Isolation Forest works well for multivariate data without distributional assumptions.
Related concept
Read the scenario before looking for a memorised answer.
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
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
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: Isolation Forest — Option C is correct because Isolation Forest is an ensemble method that isolates anomalies effectively in high-dimensional spaces without assuming distribution. Option A is wrong because Z-score assumes normal distribution. Option B is wrong because IQR is univariate. Option D is wrong because DBSCAN is for clustering, not specifically for outlier detection.
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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