- A
Reinforcement learning
Why wrong: Reinforcement learning involves an agent interacting with an environment, learning from rewards.
- B
Regression
Why wrong: Regression predicts continuous numeric values, not binary outcomes.
- C
Binary classification
Churn prediction with two classes (yes/no) is a binary classification problem.
- D
Clustering
Why wrong: Clustering is unsupervised and does not use labeled outcomes.
Question 840 of 1,000
AI0-001 AI Concepts and Techniques Practice Question
This AI0-001 practice question tests your understanding of ai concepts and techniques. 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 data scientist needs to predict whether a customer will churn (yes/no) based on historical data. Which type of machine learning problem is this?
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
This is a binary classification problem because the target variable has exactly two discrete outcomes: 'yes' (churn) or 'no' (no churn). Classification algorithms such as logistic regression, decision trees, or support vector machines are used to assign input features to one of these two predefined classes. The output is a categorical label, not a continuous value or a reward signal.
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.
- ✗
Reinforcement learning
Why it's wrong here
Reinforcement learning involves an agent interacting with an environment, learning from rewards.
- ✗
Regression
Why it's wrong here
Regression predicts continuous numeric values, not binary outcomes.
- ✓
Binary classification
Why this is correct
Churn prediction with two classes (yes/no) is a binary classification problem.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Clustering
Why it's wrong here
Clustering is unsupervised and does not use labeled outcomes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between classification and regression by presenting a binary outcome and expecting candidates to recognize it as classification, not regression, even though the term 'regression' appears in 'logistic regression' which is actually a classification algorithm.
Detailed technical explanation
How to think about this question
Binary classification models output a probability score (e.g., via a sigmoid activation function in logistic regression) that is thresholded (typically at 0.5) to assign the final class. In practice, imbalanced datasets (e.g., only 5% churn rate) require techniques like class weighting, SMOTE, or adjusting the decision threshold to avoid a model that always predicts 'no churn'. Real-world churn models often incorporate feature engineering on recency, frequency, and monetary value (RFM) to improve predictive accuracy.
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 practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Concepts and Techniques — This question tests AI Concepts and Techniques — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Binary classification — This is a binary classification problem because the target variable has exactly two discrete outcomes: 'yes' (churn) or 'no' (no churn). Classification algorithms such as logistic regression, decision trees, or support vector machines are used to assign input features to one of these two predefined classes. The output is a categorical label, not a continuous value or a reward signal.
What should I do if I get this AI0-001 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.
About these practice questions
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Last reviewed: Jul 4, 2026
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.
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