The answer is regression. This is the correct choice because predicting the exact market price of a house involves a continuous numeric target variable—price—which is the defining characteristic of a regression task. In contrast, classification would be used if the goal were to predict a discrete category, such as whether the house price is “high” or “low.” On the Microsoft Azure AI Fundamentals AI-900 exam, this distinction tests your understanding of supervised learning task types, often appearing in scenario-based questions where you must match the problem to the correct algorithm. A common trap is confusing regression with classification when the output seems like a category; remember that if the output is a precise number (like $350,000), it is regression. For a quick memory tip, think of “regression for real numbers”—both start with ‘r’—while classification sorts into classes.
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 data scientist wants to train a machine learning model to predict the exact market price of a house based on features such as square footage, number of bedrooms, and location. Which type of machine learning task should be used?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Regression
Predicting the exact market price of a house is a regression task because the target variable (price) is a continuous numeric value. Regression algorithms, such as linear regression or decision tree regression, learn the relationship between input features (e.g., square footage, bedrooms, location) and a continuous output. In Azure Machine Learning, you would select a regression model from the designer or AutoML to solve this problem.
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.
✗
Classification
Why it's wrong here
Classification predicts discrete categories or labels, not continuous numeric values like price.
✓
Regression
Why this is correct
Regression predicts a continuous numeric value, which is exactly what is needed for predicting house price.
Related concept
Read the scenario before looking for a memorised answer.
✗
Clustering
Why it's wrong here
Clustering groups unlabeled data into clusters based on similarity; it does not predict a specific numeric value.
✗
Anomaly Detection
Why it's wrong here
Anomaly Detection identifies data points that differ significantly from the majority, not suitable for predicting a continuous value.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse regression with classification because both involve supervised learning, but regression outputs a continuous number while classification outputs a discrete label.
Trap categories for this question
Similar concept trap
Clustering groups unlabeled data into clusters based on similarity; it does not predict a specific numeric value.
Detailed technical explanation
How to think about this question
Regression models minimize a loss function such as mean squared error (MSE) to fit a line or curve to the data. In Azure Machine Learning, AutoML automatically evaluates multiple regression algorithms (e.g., LightGBM, ElasticNet) and hyperparameters to find the best model. A real-world scenario is predicting house prices on a continuous scale, where even a $1 difference matters for pricing strategies.
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: Regression — Predicting the exact market price of a house is a regression task because the target variable (price) is a continuous numeric value. Regression algorithms, such as linear regression or decision tree regression, learn the relationship between input features (e.g., square footage, bedrooms, location) and a continuous output. In Azure Machine Learning, you would select a regression model from the designer or AutoML to solve this problem.
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. A hospital has a dataset with historical patient records, each labeled as either 'readmitted within 30 days' or 'not readmitted'. The hospital wants to train a model to predict which current patients are likely to be readmitted. Which type of machine learning task is this?
medium
A.Supervised regression
✓ B.Supervised classification
C.Unsupervised clustering
D.Reinforcement learning
Why B: This is a supervised classification task because the dataset contains labeled historical patient records (readmitted or not readmitted), and the goal is to predict a discrete category (binary outcome) for new patients. In Azure Machine Learning, this would use a classification algorithm like logistic regression or decision tree to assign each patient to one of the two classes.
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Question Discussion
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