DA0-002 Data Analysis Practice Question
A data scientist builds a logistic regression model to predict customer churn (yes/no). The model outputs a probability of 0.75 for a particular customer. Which of the following best describes this output?
⚠ Common exam trap
DA0-002 often tests the interpretation of logistic regression output, confusing probability with odds or accuracy. Candidates might think 0.75 means 75% accuracy or odds of 0.75 to 1.
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
✓
There is a 75% chance the customer will churn.
The output of a logistic regression model is a probability between 0 and 1. A value of 0.75 means the model estimates a 75% probability that the customer will churn (the positive class). This is a probabilistic prediction, not a certainty.
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 customer will definitely churn.
Why it's wrong here
A logistic regression output of 0.75 is the estimated probability of churn for that customer, not a certainty. It is tempting because a high value feels decisive, but the model returns a likelihood between 0 and 1; classification into churn or not requires applying a chosen threshold to that probability.
- ✓
There is a 75% chance the customer will churn.
Why this is correct
Logistic regression outputs a probability between 0 and 1 for the positive class. A value of 0.75 means the model estimates a 75% probability that this customer will churn, expressed as a likelihood rather than a certainty.
- ✗
The odds of churning are 0.75 to 1.
Why it's wrong here
Logistic regression outputs a probability, so 0.75 means a 75% chance of churn. Odds equal p/(1−p), giving 0.75/0.25 = 3 to 1, not 0.75 to 1. The option misreads the probability as an odds ratio, a tempting slip because both are expressed as decimal numbers.
- ✗
The model is 75% accurate.
Why it's wrong here
A probability of 0.75 is the model's estimated likelihood that this customer churns, not a measure of overall model performance. Accuracy is computed across a labelled test set by comparing predicted classes with actual outcomes. Confusing a per-record probability with a global accuracy metric is the trap here.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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