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.
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. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 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?
Supervised learning uses labeled data to train a model for prediction. The labeled outcomes (defective/non-defective) make this the correct approach.
B
Unsupervised learning
Why wrong: Unsupervised learning uses unlabeled data to find patterns without predefined outcomes. Since the data is labeled, this is not appropriate.
C
Reinforcement learning
Why wrong: Reinforcement learning uses an agent that learns through rewards and punishments from actions. This scenario has no interactive agent or reward system.
D
Semi-supervised learning
Why wrong: 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.
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.
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 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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
✗
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.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Trap categories for this question
Scenario analysis trap
Reinforcement learning uses an agent that learns through rewards and punishments from actions. This scenario has no interactive agent or reward system.
Detailed technical explanation
How to think about this question
Under the hood, supervised learning in Azure Machine Learning uses algorithms that minimize a loss function (e.g., log loss for classification) by adjusting model weights based on the labeled training data. A subtle behavior is that imbalanced datasets (e.g., few defective samples) can bias the model, requiring techniques like SMOTE or class weighting. In a real-world production line, sensor readings may include time-series features, so feature engineering (e.g., rolling averages) is critical for accurate defect prediction.
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 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.
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.
About these practice questions
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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.
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Question Discussion
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