Question 37 of 1,020

Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped

Is Customer Churn Prediction Binary Classification?

This AI-900 practice question tests your understanding of describe artificial intelligence workloads and considerations. 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 retail company wants to predict which customers are likely to cancel their subscription in the next 30 days. What ML task type is this?

A
Clustering to identify similar customer segments
Why wrong: Clustering finds natural groups without a target outcome — churn prediction needs a specific 'will churn' vs. 'won't churn' label.
B
Binary classification to predict whether each customer will cancel or stay
Churn prediction is binary classification — each customer is labeled as 'likely to churn' or 'not' based on their behavioral features.
C
Regression to predict the customer's lifetime value
Why wrong: Lifetime value prediction is regression (continuous value) — churn prediction is binary classification.
D
Generative AI to write personalized retention emails
Why wrong: Email generation is generative AI — identifying which customers to contact is churn prediction (classification).

Full breakdown with real-world context →

Quick Answer

The answer is binary classification because the goal is to predict one of two mutually exclusive outcomes for each customer: either they will cancel their subscription (churn) or stay (not churn) within the next 30 days. This is a classic binary classification task, where machine learning models like logistic regression or decision trees assign each input to one of two discrete labels based on patterns learned from historical data. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your ability to distinguish between classification, regression, and clustering tasks—a common trap is confusing churn prediction with regression, but since the output is a category (cancel or stay) rather than a continuous number, it is classification. The exam often presents similar retail or subscription-based examples to assess your understanding of supervised learning task types. Memory tip: think of binary classification as a “yes or no” question—here, “Will this customer churn? Yes or no?”—so if the answer is one of only two options, it is always binary classification.

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.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

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 finds natural groups without a target outcome — churn prediction needs a specific 'will churn' vs. 'won't churn' label.
✓
Binary classification to predict whether each customer will cancel or stay
Why this is correct
Churn prediction is binary classification — each customer is labeled as 'likely to churn' or 'not' based on their behavioral features.
Related concept
Read the scenario before looking for a memorised answer.
✗
Regression to predict the customer's lifetime value
Why it's wrong here
Lifetime value prediction is regression (continuous value) — churn prediction is binary classification.
✗
Generative AI to write personalized retention emails
Why it's wrong here
Email generation is generative AI — identifying which customers to contact is churn prediction (classification).

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that 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.

Detailed technical explanation

How to think about this question

Under the hood, binary classification models like logistic regression compute a probability score between 0 and 1 using a sigmoid activation function, then apply a threshold (commonly 0.5) to assign the final class label. In real-world churn prediction, imbalanced datasets (e.g., only 5% of customers churn) require techniques like class weighting or SMOTE to avoid the model simply predicting 'stay' for everyone. Azure Machine Learning's Automated ML can automatically select the best binary classifier and handle such imbalances.

KKey Concepts to Remember

Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.

TExam Day Tips

Watch for words such as best, first, most likely and least administrative effort.
Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this AI-900 question test?

Describe Artificial Intelligence workloads and considerations — This question tests Describe Artificial Intelligence workloads and considerations — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: 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.

What should I do if I get this AI-900 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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