DA0-002 Data Analysis Practice Question
A data analyst is building a supervised learning model to predict customer churn. The target variable is binary (churn = yes/no). Which TWO modeling techniques are appropriate for this task? (Select two.)
⚠ Common exam trap
Test-takers frequently confuse unsupervised clustering (K-means) or association rule mining (Apriori) with supervised classification, or mistakenly think linear regression can be adapted for binary outcomes without transformation.
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
✓
Logistic regression
Logistic regression (C) is correct because it is a supervised classification technique that models the probability of a binary outcome (churn yes/no) using a sigmoid function, making it a standard choice for binary targets. Decision trees (D) are also correct because they are supervised classifiers that recursively split features to predict categorical class labels, and they handle binary targets naturally while offering interpretability. K-means clustering (A) is wrong because it is an unsupervised algorithm that groups unlabeled data and cannot predict a labeled binary target. Linear regression (B) is wrong because it predicts continuous numeric values rather than class probabilities or discrete categories, so it is unsuitable for binary classification. The Apriori algorithm (E) is wrong because it is an unsupervised association-rule mining method for finding frequent itemsets, not a predictive classification model.
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 it's wrong here
K-means is unsupervised clustering, so it groups unlabelled records by distance to centroids and has no target variable to predict, making it unusable for binary churn classification. It would be the right choice for segmenting customers into behavioural clusters when no outcome label exists.
- ✗
Linear regression
Why it's wrong here
Linear regression predicts a continuous numeric outcome by fitting a straight-line relationship, so it cannot output a bounded yes/no class without an arbitrary threshold. Logistic regression is the correct technique for binary targets; linear regression suits continuous outcomes such as revenue or spend.
- ✓
Logistic regression
Why this is correct
Logistic regression models the probability of a binary outcome by applying the logistic (sigmoid) function to a linear combination of predictors, bounding output between 0 and 1. This directly suits the churn yes/no target, unlike ordinary linear regression.
- ✓
Decision trees
Why this is correct
Decision trees recursively partition data using feature thresholds, naturally handling binary classification by assigning churn or no-churn to leaf nodes. They require no distributional assumptions and capture non-linear interactions, making them well suited to a binary target.
- ✗
Apriori algorithm
Why it's wrong here
Apriori mines frequent itemsets and association rules from transactional data, requiring no labelled target, so it cannot classify churn. It would be correct for market-basket analysis, such as discovering which products customers buy together, not for predicting a binary outcome.
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