Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A retail company wants to predict which customers are likely to cancel their subscription in the next 30 days. What ML task type is this?
⚠ Common exam trap
A common mix-up: candidates confuse 'clustering' (unsupervised grouping) with 'classification' (supervised labeling), especially when the question mentions 'similar customer segments' in option A, which sounds plausible but is incorrect for a predictive task with a defined outcome.
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
✓
Binary classification to predict whether each customer will cancel or stay
This is a binary classification task because the goal is to predict one of two mutually exclusive outcomes for each customer: either they will cancel (churn) or stay (not churn) within the next 30 days. Binary classification algorithms, such as logistic regression or decision trees, are specifically designed to assign each input to one of two discrete labels based on learned patterns from historical data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Clustering to identify similar customer segments
Why it's wrong here
Clustering is an unsupervised technique that groups customers based on similarity in features, such as purchasing habits or engagement levels, without using any labeled outcome. Those groups, or segments, may be useful for marketing personalization but they do not inherently tell you which customers are about to cancel. Because churn prediction requires a known target variable to supervise the mapping from behavior to 'cancel' or 'stay,' clustering alone cannot make that individual-level prediction.
- ✓
Binary classification to predict whether each customer will cancel or stay
Why this is correct
This is a binary classification problem because each customer belongs to one of two mutually exclusive classes: will cancel or will stay. A supervised learning model can be trained on historical features such as usage frequency, support tickets, and payment patterns, with past outcomes used as labels. The model outputs a churn probability, and a threshold is applied to convert that probability into a predicted class, enabling the organization to target retention efforts accurately.
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Regression to predict the customer's lifetime value
Why it's wrong here
Regression predicts a continuous numeric value, such as expected revenue or customer lifetime value in dollars. Churn prediction, by contrast, has a categorical target variable with exactly two possible outcomes: the customer either cancels or stays. Fitting a regression model would treat 'cancel' as a number on an arbitrary scale, which fails to produce a proper probability of churn and does not answer the yes/no question of whether a customer will leave.
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Generative AI to write personalized retention emails
Why it's wrong here
Generative AI is designed to create new content such as text, and it could indeed draft personalized retention emails once at-risk customers are identified. However, the core task here is predicting which customers will cancel, which requires a supervised learning model that outputs a discrete churn label. Generating email copy does not analyze historical customer behavior to estimate churn risk, so it is the wrong tool for this prediction problem.
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Related to this question
Learn chapter
Regression and Classification
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
Key term
Regression
Regression is a type of machine learning algorithm that predicts a continuous numeric output based on input data, used to model relationships between variables.
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