DA0-002 Data Analysis Practice Question
A marketing team wants to segment customers into groups based on purchasing behavior without prior labels. Which algorithm should the data analyst use?
⚠ Common exam trap
A common mix-up: candidates confuse unsupervised clustering (K-means) with supervised classification (K-nearest neighbors) because both involve 'K' and grouping, but KNN requires labeled data and predicts labels, while K-means discovers inherent structures without labels.
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
✓
K-means clustering
K-means clustering is the correct choice because it is an unsupervised learning algorithm that groups unlabeled data into clusters based on feature similarity. Since the marketing team has no prior labels for customer segments, K-means can partition customers by purchasing behavior patterns, such as frequency and monetary value, without needing predefined categories.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
K-means clustering
Why this is correct
K-means is an unsupervised clustering algorithm suitable for segmentation.
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K-nearest neighbors
Why it's wrong here
KNN is a supervised learning algorithm used for classification/regression.
- ✗
Linear regression
Why it's wrong here
Linear regression is supervised and predicts continuous outcomes.
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Decision tree
Why it's wrong here
Decision trees are typically used for supervised classification or regression.
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