The answer is regression, because the goal is to predict a continuous numeric value—the sale price—based on input features like size, bedrooms, and location. Regression models learn the relationship between these independent variables and a dependent variable to output a real number, making it the correct choice for continuous price prediction. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your ability to distinguish regression from classification or clustering; a common trap is confusing price prediction with classification when the output is a dollar amount rather than a category. Remember that any task involving forecasting a quantity, such as house price or temperature, is regression, while predicting a label like “expensive” or “cheap” would be classification. A helpful memory tip is to think of “regression” as “returning a real number”—if the answer is a continuous value, it’s regression every time.
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 has a dataset containing information about houses: size (sq ft), number of bedrooms, location, and the actual sale price. The goal is to train a model that predicts the price of a new house based on these features. Which type of machine learning task is this?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
B) Regression
This is a regression task because the goal is to predict a continuous numeric value (the sale price) based on input features. Regression models learn the relationship between independent variables (size, bedrooms, location) and a dependent variable (price) to output a real number. In Azure Machine Learning, regression algorithms like Linear Regression, Decision Forest Regression, or Neural Network Regression would be appropriate for this scenario.
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.
✗
A) Classification
Why it's wrong here
Classification predicts discrete categories, not a continuous numeric value like price.
✓
B) Regression
Why this is correct
Correct. Regression models can predict a continuous numeric output such as house prices.
Related concept
Read the scenario before looking for a memorised answer.
✗
C) Clustering
Why it's wrong here
Clustering is an unsupervised method that groups unlabelled data; it does not predict a specific numeric value.
✗
D) Reinforcement Learning
Why it's wrong here
Reinforcement learning uses an agent that learns from actions and rewards, not from labeled examples of features and continuous targets.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing regression with classification because both are supervised learning, but regression outputs a continuous number while classification outputs a discrete label.
Detailed technical explanation
How to think about this question
Regression models minimize a loss function such as Mean Squared Error (MSE) between predicted and actual prices. In Azure ML, automated machine learning (AutoML) can automatically test multiple regression algorithms and hyperparameters to find the best model. A real-world nuance: location may need to be one-hot encoded or embedded to capture non-linear spatial effects on price.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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: B) Regression — This is a regression task because the goal is to predict a continuous numeric value (the sale price) based on input features. Regression models learn the relationship between independent variables (size, bedrooms, location) and a dependent variable (price) to output a real number. In Azure Machine Learning, regression algorithms like Linear Regression, Decision Forest Regression, or Neural Network Regression would be appropriate for this scenario.
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. What is the 'mean absolute error' (MAE) metric used to evaluate in machine learning?
easy
A.The average confidence percentage of classification predictions
✓ B.The average absolute difference between regression model predictions and actual values
C.The proportion of model predictions that deviate from expected values by more than a threshold
D.How much the model's predictions differ from random chance
Why B: Mean Absolute Error (MAE) is a regression metric that calculates the average of the absolute differences between predicted and actual values. It measures how close predictions are to the true outcomes, with lower values indicating better model accuracy. In Azure Machine Learning, MAE is commonly used to evaluate regression models like linear regression or decision forests.
Last reviewed: Jun 11, 2026
Question Discussion
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