The answer is the Modified Z-score using median absolute deviation (MAD), along with the IQR method and standard Z-score. These three are correct because they are specifically designed for univariate data, where you analyze a single feature for outliers by measuring its distance from a central tendency. The Modified Z-score is robust to non-normal distributions by using the median and MAD instead of the mean and standard deviation, making it ideal for real-world datasets. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between univariate and multivariate techniques; a common trap is selecting DBSCAN, which is a density-based clustering algorithm for multivariate data, or Cook’s distance, which is a regression diagnostic for multivariate influence. Remember the mnemonic “MIM” for univariate outlier detection: Modified Z-score, IQR, and Mean-based Z-score.
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.
Which THREE are common techniques for detecting outliers in a univariate dataset? (Select THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Z-score
Options A, C, and D are correct. Option B is wrong because DBSCAN is a multivariate clustering method. Option E is wrong because Cook's distance is for regression diagnostics.
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.
✗
Cook's distance
Why it's wrong here
Cook's distance is for identifying influential points in regression.
✗
DBSCAN clustering
Why it's wrong here
DBSCAN is used for clustering, not univariate outlier detection.
✓
Z-score
Why this is correct
Z-score measures how many standard deviations an observation is from the mean.
Related concept
Read the scenario before looking for a memorised answer.
✓
Interquartile range (IQR) method
Why this is correct
Points outside 1.5*IQR from quartiles are outliers.
Related concept
Read the scenario before looking for a memorised answer.
✓
Modified Z-score using median absolute deviation (MAD)
Why this is correct
MAD is robust to outliers and used for univariate detection.
Related concept
Read the scenario before looking for a memorised answer.
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: Z-score — Options A, C, and D are correct. Option B is wrong because DBSCAN is a multivariate clustering method. Option E is wrong because Cook's distance is for regression diagnostics.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO of the following are common techniques for detecting outliers in a numerical feature?
easy
A.Chi-square test
B.Standard deviation
✓ C.Interquartile Range (IQR)
✓ D.Z-score
E.Principal Component Analysis (PCA)
Why C: Z-score and IQR are standard outlier detection methods. PCA can detect outliers but is not a common direct method. Chi-square is for categorical association. Standard deviation alone is not a method.
Variation 2. Which THREE techniques are commonly used to detect outliers in a dataset? (Select THREE.)
medium
✓ A.Interquartile range (IQR)
B.k-means clustering
C.Principal component analysis (PCA)
✓ D.Z-score
✓ E.Isolation Forest
Why A: Options A, B, and D are correct. Z-score and IQR are standard statistical methods, and isolation forest is a machine learning algorithm for anomaly detection. Option C is wrong because PCA is for dimensionality reduction, not outlier detection, though it can be used in some contexts but is not common. Option E is wrong because k-means clustering is for clustering, not specifically for outlier detection.
Last reviewed: Jun 20, 2026
Question Discussion
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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.
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