The answer is supervised classification, specifically binary classification, because the goal is to predict a categorical outcome—whether a customer will churn (yes or no)—using historical labeled data where the target variable is already known. This type of machine learning learns patterns from input features like age and purchase history to assign new customers to one of two discrete classes, making it the correct approach for churn prediction. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your ability to distinguish between supervised and unsupervised learning, with a common trap being to confuse regression (which predicts continuous values) with classification. Remember the key clue: if the output is a yes/no or true/false label, it is always supervised classification. A helpful memory tip is to think of “binary” as having two sides—like a coin flip—so whenever you see a two-category outcome, binary classification is your go-to model.
AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
This AI-900 practice question tests your understanding of describe fundamental principles of machine learning on azure. 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 has historical data about customers, including age, purchase history, and whether they have churned (yes/no). They want to train a model that predicts if a new customer will churn. Which type of machine learning should they use?
Why wrong: Regression predicts a continuous numeric value, not a binary category.
B
Supervised classification
Classification predicts a discrete category. Churn prediction is a classic binary classification problem.
C
Unsupervised clustering
Why wrong: Clustering finds hidden patterns in unlabeled data; it does not use predefined labels.
D
Reinforcement learning
Why wrong: Reinforcement learning uses an agent that learns by interacting with an environment and receiving rewards; it is not suitable for labeled historical data.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Supervised classification
The goal is to predict a categorical outcome (churn: yes/no) from historical labeled data. Supervised classification algorithms, such as logistic regression or decision trees, learn from input features (age, purchase history) and the target label (churn status) to assign new customers to one of the discrete classes. This directly matches the requirement for a binary classification model.
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.
✗
Supervised regression
Why it's wrong here
Regression predicts a continuous numeric value, not a binary category.
✓
Supervised classification
Why this is correct
Classification predicts a discrete category. Churn prediction is a classic binary classification problem.
Related concept
Read the scenario before looking for a memorised answer.
✗
Unsupervised clustering
Why it's wrong here
Clustering finds hidden patterns in unlabeled data; it does not use predefined labels.
✗
Reinforcement learning
Why it's wrong here
Reinforcement learning uses an agent that learns by interacting with an environment and receiving rewards; it is not suitable for labeled historical data.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse regression with classification when the output is a binary yes/no, mistakenly thinking any numeric prediction task is regression, but classification is required for discrete categorical outcomes.
Detailed technical explanation
How to think about this question
Under the hood, a binary classification model like logistic regression estimates the probability that a customer belongs to the positive class (churn=yes) using a sigmoid function, then applies a threshold (typically 0.5) to make the final decision. In real-world retail scenarios, imbalanced classes (e.g., only 5% churn) require techniques like class weighting or SMOTE to avoid a model that always predicts 'no churn' and still achieves high accuracy. Azure Machine Learning provides automated ML that can test multiple classifiers and tune hyperparameters to optimize for metrics like AUC or F1-score.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe fundamental principles of machine learning on Azure — This question tests Describe fundamental principles of machine learning on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Supervised classification — The goal is to predict a categorical outcome (churn: yes/no) from historical labeled data. Supervised classification algorithms, such as logistic regression or decision trees, learn from input features (age, purchase history) and the target label (churn status) to assign new customers to one of the discrete classes. This directly matches the requirement for a binary classification model.
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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Variation 1. What is the role of a label (also called target or ground truth) in supervised machine learning?
easy
A.A category of input features used by the model
✓ B.The correct output or answer associated with each training example that the model learns to predict
C.A text description attached to a model explaining what it does
D.A tag applied to Azure ML resources for organization
Why B: In supervised machine learning, the label (also called target or ground truth) is the known correct output for each training example. The model uses these labels during training to learn the mapping from input features to outputs, enabling it to make accurate predictions on new, unseen data. This is fundamental to supervised learning, where the algorithm minimizes the error between its predictions and the ground truth labels.
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Question Discussion
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