MLS-C01 Exploratory Data Analysis Practice Question

Which TWO of the following are common techniques for detecting outliers in a dataset?

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Z-score

Z-score identifies outliers based on standard deviations from the mean. IQR method uses quartile ranges to flag points outside 1.5*IQR. Standard scaling, PCA, and K-means are not primarily outlier detection methods.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Z-score
Why this is correct
Z-score measures how many standard deviations a point is from the mean; values beyond a threshold (e.g., 3) are outliers.
✓
Interquartile range (IQR) method
Why this is correct
IQR method flags points below Q1-1.5*IQR or above Q3+1.5*IQR as outliers.
✗
Principal Component Analysis (PCA)
Why it's wrong here
PCA is a dimensionality reduction technique, not an outlier detection method.
✗
K-means clustering
Why it's wrong here
K-means clusters data but does not directly identify outliers; it can be used for novelty detection after training.
✗
Standard scaling
Why it's wrong here
Standard scaling normalizes features but does not detect outliers.

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MLS-C01 Exploratory Data Analysis Practice Question

Why each option matters

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