Supervised Learning with Labeled Data
A manufacturing team wants to predict product defects based on sensor readings from the production line. They have 10,000 historical samples, each labeled as 'defective' or 'non-defective'. Which type of machine learning should they use in Azure Machine Learning?
Quick Answer
The answer is supervised learning. This is correct because the manufacturing team has 10,000 historical samples, each labeled as 'defective' or 'non-defective', and the goal is to predict a categorical outcome based on sensor readings. Supervised learning relies on labeled data to train a model that maps input features to known outputs, making it ideal for classification tasks like defect prediction. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your ability to distinguish between supervised and unsupervised learning examples: if the dataset includes labels, it is supervised; if it lacks labels and seeks hidden patterns, it is unsupervised. A common trap is confusing regression (predicting a continuous value) with classification (predicting a category), but here the binary 'defective' or 'non-defective' label clearly indicates a classification problem. In Azure Machine Learning, you would use algorithms like two-class logistic regression or boosted decision trees. Memory tip: think "Supervised = Supervised by labels" — if you have the answers in the data, it's supervised.
⚠ Common exam trap
Candidates often confuse 'predicting defects' with unsupervised anomaly detection, but the presence of explicit labels (defective/non-defective) makes this a supervised classification task, not an unsupervised one.
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
✓
Supervised learning
This is a supervised learning problem because the dataset contains labeled historical samples (defective or non-defective), and the goal is to predict a categorical outcome based on sensor readings. In Azure Machine Learning, supervised learning algorithms such as two-class logistic regression or boosted decision trees are used to train a model that maps input features to known labels.
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
Why this is correct
Supervised learning uses labeled data to train a model for prediction. The labeled outcomes (defective/non-defective) make this the correct approach.
- ✗
Unsupervised learning
Why it's wrong here
Unsupervised learning uses unlabeled data to find patterns without predefined outcomes. Since the data is labeled, this is not appropriate.
When this WOULD be correct
If the question described a scenario with no labeled data, such as 'grouping sensor readings into clusters to discover unknown defect patterns', then unsupervised learning (e.g., clustering) would be correct.
- ✗
Reinforcement learning
Why it's wrong here
Reinforcement learning uses an agent that learns through rewards and punishments from actions. This scenario has no interactive agent or reward system.
When this WOULD be correct
A manufacturing robot must learn to adjust its arm movements to minimize defects over time based on trial-and-error feedback. The question would specify that the model learns from rewards (e.g., penalty for defects) rather than from labeled historical data.
- ✗
Semi-supervised learning
Why it's wrong here
Semi-supervised learning uses a small amount of labeled data with a larger amount of unlabeled data. Here all 10,000 samples are labeled, so it is not the best fit.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Supervised learningCorrect answer▾
Why this is correct
Supervised learning uses labeled data to train a model for prediction. The labeled outcomes (defective/non-defective) make this the correct approach.
✗Unsupervised learningWrong answer — click to see why▾
Why this is wrong here
The dataset has labeled samples ('defective' or 'non-defective'), so the problem is supervised learning. Unsupervised learning is for unlabeled data, making it incorrect here.
★ When this WOULD be the correct answer
If the question described a scenario with no labeled data, such as 'grouping sensor readings into clusters to discover unknown defect patterns', then unsupervised learning (e.g., clustering) would be correct.
Why candidates choose this
Candidates may think 'predicting defects' sounds like discovering patterns without labels, confusing unsupervised clustering with supervised classification.
✗Reinforcement learningWrong answer — click to see why▾
Why this is wrong here
Reinforcement learning is used for sequential decision-making with reward signals, not for predicting defects from labeled historical data. The question involves labeled samples (defective/non-defective), which is a supervised learning task.
★ When this WOULD be the correct answer
A manufacturing robot must learn to adjust its arm movements to minimize defects over time based on trial-and-error feedback. The question would specify that the model learns from rewards (e.g., penalty for defects) rather than from labeled historical data.
Why candidates choose this
Candidates may confuse reinforcement learning with any learning that involves 'feedback' or 'improvement over time', not realizing that the presence of labeled historical data makes it supervised learning.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Supervised learning
Supervised learning is a type of machine learning where a model learns from labeled training data to make predictions or decisions.
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.
About these practice questions
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Same concept, more angles
1 more way this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist wants to group customers into segments based on purchasing behavior without using any labeled examples. Which type of machine learning is this?
easy- A.Supervised learning
- ✓ B.Unsupervised learning
- C.Reinforcement learning
- D.Semi-supervised learning
Why B: Unsupervised learning is the correct choice because the data scientist has no labeled examples and wants to discover hidden patterns or groupings in the data. Clustering algorithms, such as K-Means or DBSCAN, are used to segment customers based solely on their purchasing behavior features, without any predefined categories.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.