DA0-002 Data Analysis Practice Question
A data scientist is performing K-means clustering on customer data. She plots the within-cluster sum of squares (WCSS) for different values of k and observes an 'elbow' at k=4. What does this indicate?
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
✓
The optimal number of clusters is 4
The elbow method suggests that adding more clusters beyond k=4 yields diminishing returns, so k=4 is a suitable number of clusters.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The optimal number of clusters is 4
Why this is correct
The elbow point indicates a good trade-off between cluster compactness and number of clusters.
- ✗
The algorithm should be run with k=3 to avoid overfitting
Why it's wrong here
Elbow suggests k=4, not k=3.
- ✗
The data contains exactly 4 outliers
Why it's wrong here
Elbow method does not identify outliers.
- ✗
The WCSS is minimized at k=4, indicating perfect clustering
Why it's wrong here
WCSS always decreases as k increases; the elbow is a heuristic, not a minimization.
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