The answer is Isolation Forest, as it is the most appropriate method for outlier detection in high-dimensional space. Unlike traditional techniques, Isolation Forest leverages tree-based isolation by randomly selecting features and split values to isolate anomalies, which become shorter paths in the tree structure—this makes it highly effective when dealing with many features, where distance-based metrics often fail due to the curse of dimensionality. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of why univariate methods like Z-score or IQR are unsuitable for high-dimensional data, and why Mahalanobis distance, which assumes multivariate normality, becomes unreliable as dimensionality grows. A common trap is choosing Mahalanobis distance because it seems multivariate, but it breaks down with 100 features. Memory tip: think "Isolation Forest isolates outliers fast in high dimensions—like finding a needle in a haystack by splitting the hay, not measuring every straw."
MLS-C01 Exploratory Data Analysis Practice Question
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 working with a dataset containing 10,000 observations and 100 features. The scientist wants to detect outliers in the dataset. Which method is most appropriate for outlier detection in a high-dimensional space?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Isolation Forest
Option D is correct because Isolation Forest is effective for high-dimensional data and uses tree-based isolation. Option A is wrong because Z-score assumes normality and is univariate. Option B is wrong because IQR is univariate and not suitable for high dimensions. Option C is wrong because Mahalanobis distance assumes multivariate normality and is sensitive to dimensionality.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Use Z-score to identify points beyond 3 standard deviations
Why it's wrong here
Z-score is univariate and assumes normal distribution.
✓
Use Isolation Forest
Why this is correct
Isolation Forest is designed for high-dimensional data and does not assume distribution.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Mahalanobis distance
Why it's wrong here
Mahalanobis distance assumes multivariate normality and can be unstable in high dimensions.
✗
Use interquartile range (IQR) for each feature
Why it's wrong here
IQR is univariate and ignores correlations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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
How to think about this question
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.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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. 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
Got this wrong? Here's your next step.
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.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Isolation Forest — Option D is correct because Isolation Forest is effective for high-dimensional data and uses tree-based isolation. Option A is wrong because Z-score assumes normality and is univariate. Option B is wrong because IQR is univariate and not suitable for high dimensions. Option C is wrong because Mahalanobis distance assumes multivariate normality and is sensitive to dimensionality.
What should I do if I get this MLS-C01 question wrong?
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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