DA0-002 Data Analysis Practice Question
A data analyst wants to segment customers based on purchasing behavior such as frequency, monetary value, and recency. Which TWO clustering evaluation methods can help determine the optimal number of clusters? (Select two.)
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
✓
Silhouette score
The elbow method uses within-cluster sum of squares, and the silhouette score measures cohesion and separation. Both help choose k. Correlation coefficient is for association, not clustering. ANOVA and t-test are for hypothesis testing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Correlation coefficient
Why it's wrong here
A correlation coefficient measures the linear relationship between two variables, so it cannot score cluster partitions for a k value. It is tempting because it quantifies feature association, and it would be correct when selecting which RFM variables to retain before clustering.
- ✗
ANOVA
Why it's wrong here
ANOVA compares means across three or more predefined groups, so it cannot determine the optimal cluster count. It is tempting because it handles multiple groups, and it would be correct when testing whether mean spend differs significantly across segments already produced by clustering.
- ✓
Silhouette score
Why this is correct
The silhouette score measures how similar each point is to its own cluster versus the nearest other cluster, producing a coefficient between -1 and 1. The cluster count with the highest average silhouette indicates the best-separated segmentation.
- ✗
t-test
Why it's wrong here
A t-test compares means between two groups, so it cannot evaluate partition quality across candidate k values. It is tempting because it is a familiar statistical significance test, and it would be correct when testing whether two customer segments differ on average monetary value.
- ✓
Elbow method
Why this is correct
The elbow method plots within-cluster sum of squared distances against cluster count, revealing the point where adding clusters yields diminishing variance reduction. That inflection guides the optimal k for segmenting customers on recency, frequency and monetary value.
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