AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist at a retail company has a dataset of customer transactions with features such as age, income, and purchase history. The goal is to group customers into distinct segments based on similarities in these features, without any predefined labels. Which type of machine learning should the data scientist use?
⚠ Common exam trap
Many exam-takers confuse clustering with classification because both involve grouping, but classification requires predefined labels while clustering discovers groups without labels.
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
✓
Unsupervised learning – clustering
The scenario requires grouping customers into segments without predefined labels, which is a classic unsupervised learning task. Clustering algorithms identify patterns and similarities in data to form groups. Supervised learning methods like classification and regression need labeled data, which is not present. Reinforcement learning is for interactive decision-making, not static segmentation. Therefore, clustering is the correct machine learning type.
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 learning – classification
Why it's wrong here
Classification is a supervised learning technique that requires labeled training data to predict discrete categories. In this scenario, there are no predefined labels or categories for the customer segments; the goal is to discover natural groupings. Therefore, classification is not suitable because it would need to know the segment labels in advance, which are not available here.
- ✗
Supervised learning – regression
Why it's wrong here
Regression is a supervised learning method used to predict continuous numeric values, such as sales amounts or temperatures. The scenario does not involve predicting a continuous value; instead, it requires grouping customers into segments. Regression would require a labeled target variable, which is absent, so it cannot achieve the desired segmentation.
- ✗
Reinforcement learning
Why it's wrong here
Reinforcement learning trains an agent to make decisions by receiving rewards or penalties from an environment. It is typically used for sequential decision-making tasks like robotics or game playing. This scenario involves static customer data and a one-time segmentation task, not an interactive environment with rewards, so reinforcement learning is not applicable.
- ✓
Unsupervised learning – clustering
Why this is correct
Clustering is an unsupervised learning technique that groups data points based on similarity without predefined labels. Here, the data scientist wants to segment customers using features like age, income, and purchase history, and no labels exist. Clustering algorithms such as K-means can identify natural groupings, making this the correct approach for this scenario.
Go deeper
Related to this question
Learn chapter
Supervised vs Unsupervised Learning
Key term
Unsupervised learning
Unsupervised learning is a type of machine learning where an algorithm finds patterns, groupings, or structure in data without being given labeled examples or correct answers.
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.
About these practice questions
This AI-900 question is part of Courseiva's 985-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
This AI-900 practice question is part of Courseiva's free Microsoft 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 AI-900 exam.