The answer is the Z-score method and the interquartile range (IQR) approach. The Z-score technique detects outliers in a univariate continuous feature by measuring how many standard deviations a data point lies from the mean, with a common threshold of an absolute Z-score greater than 3, since roughly 99.7% of normally distributed data falls within three standard deviations. The IQR method flags values below Q1 minus 1.5 times the IQR or above Q3 plus 1.5 times the IQR, making it robust to non-normal distributions. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this tests your understanding of feature engineering and data preprocessing, often appearing in scenario-based questions where you must choose the correct statistical technique for cleaning skewed or normally distributed data. A common trap is assuming Z-score works well for all distributions—it fails with heavy skew, where IQR is safer. Memory tip: “Z for normal bell, IQR for skewed well.”
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 TWO of the following are appropriate techniques for detecting outliers in a univariate continuous feature?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Z-score and flag values with absolute Z-score > 3.
The Z-score method (Option B) is a standard statistical technique for detecting outliers in a univariate continuous feature. It measures how many standard deviations a data point is from the mean, and flagging values with an absolute Z-score greater than 3 is a common threshold because, under a normal distribution, approximately 99.7% of data falls within three standard deviations, making points beyond this likely outliers.
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.
✗
Apply a Random Forest classifier to predict outliers.
Why it's wrong here
Outlier detection is unsupervised; Random Forest requires labels.
✓
Use Z-score and flag values with absolute Z-score > 3.
Why this is correct
Z-score >3 is a common outlier threshold.
Related concept
Read the scenario before looking for a memorised answer.
✗
Remove any value that is more than one standard deviation from the mean.
Why it's wrong here
One standard deviation includes 68% of data; too aggressive.
✗
Use DBSCAN clustering with default parameters.
Why it's wrong here
DBSCAN is for multivariate data and requires parameter tuning.
✓
Use the interquartile range (IQR) and flag values below Q1 - 1.5*IQR or above Q3 + 1.5*IQR.
Why this is correct
IQR method is standard for univariate outlier detection.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that removing values more than one standard deviation from the mean is a valid outlier detection technique, when in fact it removes a large portion of normal data and is not a standard practice.
Detailed technical explanation
How to think about this question
The Z-score method assumes the data is approximately normally distributed; for skewed distributions, a modified Z-score using the median and median absolute deviation (MAD) is more robust. The IQR method (Option E) is non-parametric and works well for skewed data, as it relies on quartiles rather than mean and standard deviation, making it less sensitive to extreme values. In practice, both methods can be used together to cross-validate outlier candidates, especially in datasets with mixed distributions.
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.
TExam Day Tips
→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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 Z-score and flag values with absolute Z-score > 3. — The Z-score method (Option B) is a standard statistical technique for detecting outliers in a univariate continuous feature. It measures how many standard deviations a data point is from the mean, and flagging values with an absolute Z-score greater than 3 is a common threshold because, under a normal distribution, approximately 99.7% of data falls within three standard deviations, making points beyond this likely outliers.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 appropriate techniques for detecting outliers in a univariate continuous dataset? (Select TWO.)
easy
✓ A.Z-score method
✓ B.IQR (Interquartile Range) method
C.Box plot visualization
D.Pearson correlation coefficient
E.K-means clustering
Why A: Options B and D are correct. IQR-based outlier detection identifies points beyond 1.5*IQR from quartiles. Z-score method flags points beyond a threshold (e.g., 3) from mean. Option A is wrong because clustering is multivariate. Option C is wrong because box plots visualize outliers but are not a detection technique per se; they use IQR. Option E is wrong because correlation is bivariate.
Variation 2. Which TWO are appropriate techniques for detecting outliers in a dataset during exploratory data analysis?
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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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