Question 575 of 985
Regression vs Classification in Machine Learning
A data scientist is building a model to predict the exact temperature in degrees Celsius based on humidity and atmospheric pressure. The model will output a single numeric value for each input. Which type of machine learning task is this?
Quick Answer
The answer is regression because predicting a continuous numeric value like temperature in degrees Celsius is the defining characteristic of a regression task. In machine learning, regression models output a real number based on input features—here, humidity and atmospheric pressure—whereas classification would assign discrete labels such as “hot” or “cold.” On the Microsoft Azure AI-900 exam, this distinction tests your understanding of supervised learning types; a common trap is confusing regression with classification when the output is a number, but remember that regression predicts a quantity, not a category. For Azure Machine Learning, algorithms like Linear Regression or Decision Forest Regression handle such tasks. A helpful memory tip: think of “regression” as “regressing to a real number”—if the output can be any value on a scale, it’s regression, not classification.
⚠ Common exam trap
Test-takers frequently confuse predicting a numeric value with classification, but classification outputs discrete labels (e.g., 'high temperature' vs 'low temperature'), not a precise continuous number like degrees Celsius.
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
✓
Regression
This is a regression task because the goal is to predict a continuous numeric value (temperature in degrees Celsius) from input features (humidity and atmospheric pressure). Regression models output a real number, unlike classification which predicts discrete categories. In Azure Machine Learning, regression algorithms like Linear Regression or Decision Forest Regression are used for such tasks.
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 (e.g., hot/cold), not a continuous numeric value like temperature.
When this WOULD be correct
A question where the model predicts a category, such as 'hot', 'warm', or 'cold' based on humidity and pressure, would make classification correct.
- ✓
Regression
Why this is correct
Regression predicts a continuous numeric value, such as temperature, based on input features.
- ✗
Clustering
Why it's wrong here
Clustering groups data points without predefined labels; it does not predict a numeric output.
When this WOULD be correct
A question asking to group weather data (e.g., temperature, humidity, pressure) into distinct climate zones without predefined labels would make clustering correct.
- ✗
Object detection
Why it's wrong here
Object detection identifies and locates objects within images, which is not relevant to numeric prediction.
When this WOULD be correct
A question asking for a model that identifies and locates multiple objects (e.g., cars, pedestrians) in an image, outputting bounding boxes and class labels, would make object detection correct.
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.
✓RegressionCorrect answer▾
Why this is correct
Regression predicts a continuous numeric value, such as temperature, based on input features.
✗ClassificationWrong answer — click to see why▾
Why this is wrong here
Classification predicts discrete labels or categories, not continuous numeric values. The question asks for a single numeric temperature value, which is a regression task.
★ When this WOULD be the correct answer
A question where the model predicts a category, such as 'hot', 'warm', or 'cold' based on humidity and pressure, would make classification correct.
Why candidates choose this
Candidates may confuse predicting a numeric value with classification, especially if they think of temperature ranges as categories, but the exact value requirement indicates regression.
✗ClusteringWrong answer — click to see why▾
Why this is wrong here
Clustering groups data into clusters without labeled outputs, but this question requires predicting a continuous numeric value (temperature) from inputs, which is a regression task.
★ When this WOULD be the correct answer
A question asking to group weather data (e.g., temperature, humidity, pressure) into distinct climate zones without predefined labels would make clustering correct.
Why candidates choose this
Candidates may confuse clustering with regression because both involve numerical data, but clustering is unsupervised and does not predict a specific numeric output.
✗Object detectionWrong answer — click to see why▾
Why this is wrong here
Object detection is used to identify and locate objects within images or videos, not to predict a continuous numeric value like temperature from numerical inputs.
★ When this WOULD be the correct answer
A question asking for a model that identifies and locates multiple objects (e.g., cars, pedestrians) in an image, outputting bounding boxes and class labels, would make object detection correct.
Why candidates choose this
Candidates may confuse object detection with regression because both involve outputting numeric values (coordinates vs. temperature), but object detection deals with spatial localization in visual data.
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?”
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Same concept, more angles
3 more ways 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 is building a machine learning model to predict the number of daily bike rentals in a city based on weather data and day of the week. The target variable is a continuous integer. Which type of machine learning task is this?
medium- A.Classification
- ✓ B.Regression
- C.Clustering
- D.Anomaly Detection
Why B: The target variable is the number of daily bike rentals, which is a continuous integer (count). Predicting a continuous numeric value is a regression task. In Azure Machine Learning, regression algorithms such as Linear Regression, Decision Forest Regression, or Poisson Regression are used for this type of problem.
Variation 2. A city's traffic department wants to predict the number of cars that will cross a particular bridge each day to plan maintenance schedules. The output of the model should be a numerical value representing the estimated traffic count. Which type of machine learning task is this?
medium- A.Classification
- ✓ B.Regression
- C.Clustering
- D.Reinforcement learning
Why B: Regression is the correct type of machine learning task because the goal is to predict a continuous numerical value—the number of cars crossing the bridge each day. Unlike classification, which predicts discrete categories, regression models output a real number, making it ideal for forecasting traffic counts.
Variation 3. A data scientist trains a model to predict the exact number of cars that will cross a bridge each day for maintenance planning. The model uses historical traffic data as input. Which type of machine learning task is this?
medium- A.Classification
- ✓ B.Regression
- C.Clustering
- D.Reinforcement learning
Why B: The model predicts a continuous numerical value (the exact number of cars) based on historical traffic data. Regression is the correct machine learning task for predicting continuous numeric outcomes, such as counts, prices, or temperatures, making option B correct.
Last reviewed: Jun 11, 2026
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