AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist has a dataset containing customer transaction records with features such as age, income, and purchase history, but no labels. The goal is to identify natural groupings of customers for a targeted marketing campaign. Which type of machine learning should be used?
⚠ Common exam trap
A common mix-up: candidates confuse clustering with classification because both involve grouping, but classification requires pre-labeled categories while clustering discovers them from unlabeled data.
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
✓
Clustering
Clustering is the correct choice because the dataset has no labels, and the goal is to discover natural groupings of customers based on feature similarity. Unsupervised learning algorithms like K-Means or DBSCAN partition data into clusters where intra-cluster similarity is high and inter-cluster similarity is low, enabling targeted marketing without pre-existing categories.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Classification
Why it's wrong here
Classification is a supervised learning technique that requires a labeled dataset with discrete target classes, such as predicting 'fraud' vs. 'not fraud' from historical labels. Since the customer transaction dataset is unlabeled and the goal is to discover intrinsic patterns rather than assign predefined categories, classification cannot be applied directly. Without ground-truth labels to train on, a classifier would have no target variable to learn from.
- ✗
Regression
Why it's wrong here
Regression is a supervised learning method used to predict a continuous numeric outcome, such as sales revenue or house price, based on labeled examples. The problem described does not involve predicting a numeric value, and no labeled response variable exists in the customer transaction dataset. Because regression relies on labeled target values for training, it fails when the objective is to explore unknown structure in unlabeled data.
- ✓
Clustering
Why this is correct
Clustering is an unsupervised learning technique that partitions data points into groups based on feature similarity, requiring no predefined labels. For a dataset of customer transactions, clustering can reveal natural segments, such as purchasing-behavior clusters, by measuring distances among features like amount, frequency, and category. This directly matches the task of discovering hidden groupings without prior knowledge of class membership.
- ✗
Reinforcement learning
Why it's wrong here
Reinforcement learning trains an agent to take sequential actions in an environment to maximize cumulative rewards through trial and error, using feedback from rewards and penalties. The customer transaction dataset is static and has no notion of an agent, environment, actions, or reward signal. Finding natural groupings in a one-shot dataset is a pattern-discovery problem, not a decision-making problem, so reinforcement learning is not appropriate.
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Related to this question
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Machine Learning Core Concepts
Key term
Clustering
Clustering is a technique where multiple servers work together as a single system to keep applications running even if one server fails.
Key term
Machine learning
Machine learning is a branch of artificial intelligence where computers learn patterns from data to make decisions or predictions without being explicitly programmed for every task.
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