- A
A model that predicts which category an item belongs to from a set of options
Why wrong: Category prediction is classification — regression predicts continuous numerical values.
- B
Predicting a continuous numerical value such as price, temperature, or demand
Regression outputs a number — house price prediction, energy demand forecasting, and revenue estimation are classic regression tasks.
- C
Going back to a previous model version when the current version performs poorly
Why wrong: Version rollback is MLOps — regression in ML means predicting numerical values, not reverting changes.
- D
A technique for reducing the dimensionality of training data before model fitting
Why wrong: Dimensionality reduction is PCA/autoencoders — regression is the ML task of predicting continuous numerical outputs.
Quick Answer
The answer is predicting a continuous numerical value such as price, temperature, or demand. Regression is a supervised machine learning technique that models the relationship between input features and a dependent variable with a real-valued output, meaning it forecasts any number along a scale rather than a category or class. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your ability to distinguish regression from classification (which predicts discrete labels) and clustering (which groups unlabeled data). A common trap is confusing regression with classification when the output seems numeric but is actually ordinal or categorical—for example, predicting a rating from 1 to 5 is still regression if the output is treated as a continuous score. To remember, think of the word “regress” as moving back to a trend line: if you can plot the output on a number line with decimals, it’s regression. A handy mnemonic is “R for Real numbers”—regression always outputs a real, continuous value.
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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
What is 'regression' in machine learning and when is it used?
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
Predicting a continuous numerical value such as price, temperature, or demand
Regression is a supervised machine learning technique used to predict a continuous numerical value, such as price, temperature, or demand, based on input features. It models the relationship between independent variables and a dependent variable that has a real-valued output, making option B correct.
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 model that predicts which category an item belongs to from a set of options
Why it's wrong here
Category prediction is classification — regression predicts continuous numerical values.
- ✓
Predicting a continuous numerical value such as price, temperature, or demand
Why this is correct
Regression outputs a number — house price prediction, energy demand forecasting, and revenue estimation are classic regression tasks.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Going back to a previous model version when the current version performs poorly
Why it's wrong here
Version rollback is MLOps — regression in ML means predicting numerical values, not reverting changes.
- ✗
A technique for reducing the dimensionality of training data before model fitting
Why it's wrong here
Dimensionality reduction is PCA/autoencoders — regression is the ML task of predicting continuous numerical outputs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse regression with classification, as both are supervised learning, but regression outputs a continuous number while classification outputs a discrete label.
Trap categories for this question
Command / output trap
Dimensionality reduction is PCA/autoencoders — regression is the ML task of predicting continuous numerical outputs.
Detailed technical explanation
How to think about this question
Regression algorithms, such as linear regression or decision tree regression, minimize a loss function (e.g., mean squared error) to fit a line or curve to the training data. In Azure Machine Learning, regression models can be built using automated ML, which automatically selects the best algorithm and hyperparameters for continuous target variables. A real-world scenario is predicting housing prices based on features like square footage and location, where the output is a continuous dollar amount.
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.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
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: Predicting a continuous numerical value such as price, temperature, or demand — Regression is a supervised machine learning technique used to predict a continuous numerical value, such as price, temperature, or demand, based on input features. It models the relationship between independent variables and a dependent variable that has a real-valued output, making option B correct.
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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Last reviewed: Jun 11, 2026
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.
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