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 is appropriate because it models the probability of a binary outcome (churn yes/no) using a logistic function, making it a standard choice for binary classification tasks. It outputs a value between 0 and 1, which can be thresholded to predict the class label.
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 an unsupervised technique, not suitable for supervised classification.
- ✗
Linear regression
Why it's wrong here
Linear regression predicts continuous values, not binary outcomes.
- ✓
Logistic regression
Why this is correct
Logistic regression models binary outcomes and is appropriate for classification.
- ✓
Decision trees
Why this is correct
Decision trees can handle classification tasks and are appropriate for binary outcomes.
- ✗
Apriori algorithm
Why it's wrong here
Apriori is used for association rule mining, not classification.
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