This DA0-001 practice question tests your understanding of analyzing and modeling data. 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.
A marketing team wants to segment customers into groups based on purchasing behavior without prior labels. Which algorithm should the data analyst use?
K-means clustering
K-means is an unsupervised clustering algorithm suitable for segmentation.
K-nearest neighbors
Why wrong: KNN is a supervised learning algorithm used for classification/regression.
Linear regression
Why wrong: Linear regression is supervised and predicts continuous outcomes.
Decision tree
Why wrong: Decision trees are typically used for supervised classification or regression.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
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.
Decision tree
Why it's wrong here
Decision trees are typically used for supervised classification or regression.
Common exam traps
The trap here is that candidates often 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.
Detailed technical explanation
K-means clustering works by initializing K centroids, iteratively assigning each data point to the nearest centroid based on Euclidean distance, and recalculating centroids as the mean of assigned points until convergence. A subtle behavior is that the algorithm is sensitive to initial centroid placement and assumes spherical cluster shapes, so techniques like K-means++ initialization or scaling features (e.g., using z-scores for purchase amounts) are critical for meaningful segmentation. In a real-world scenario, a marketing team might use the elbow method to determine the optimal K by plotting within-cluster sum of squares against K.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A network engineer at a university connects two campus buildings via a fibre link. Both routers run OSPF, but no adjacency forms — even though both routers can ping each other. The engineer finds one router is in area 0 and the other in area 1. OSPF adjacency requires matching area numbers, hello/dead timers, and network type. IP reachability alone is not enough.
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FAQ
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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