- A
Classification
Why wrong: Classification predicts discrete categories (yes/no, cat/dog) — regression predicts continuous numeric values.
- B
Clustering
Why wrong: Clustering groups similar items without predefined labels — regression predicts a specific numeric output.
- C
Regression
Regression predicts continuous numeric values like prices, temperatures, or scores from input features.
- D
Anomaly detection
Why wrong: Anomaly detection identifies unusual data points — regression predicts continuous values for normal predictions.
Question 606 of 1,020
Which AI Workload Type Predicts a Continuous Numeric Value?
This AI-900 practice question tests your understanding of describe artificial intelligence workloads and considerations. 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.
Which AI workload type attempts to predict a continuous numeric value based on input features?
Quick Answer
The answer is regression, as it is the AI workload type specifically designed to predict a continuous numeric value from input features. Unlike classification, which outputs discrete categories, regression models learn a mapping function to estimate a real number—such as a price, temperature, or sales amount—by minimizing the difference between predicted and actual values. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your ability to distinguish between regression and classification workloads, often appearing in scenario-based questions where you must identify the correct task for predicting a numeric outcome. A common trap is confusing regression with classification when the output seems numerical but is actually categorical (e.g., predicting “1” for yes and “0” for no is still classification). To remember, think of the word “regress” as moving toward a continuous line—like a trend line on a graph—while “classify” sorts into buckets.
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
Regression is the correct AI workload type because it is specifically designed to predict a continuous numeric value (e.g., price, temperature, sales amount) based on input features. Unlike classification, which predicts discrete categories, regression models output a real number by learning a mapping function from the input variables to a continuous target variable, often using algorithms like linear regression, decision trees, or neural networks.
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 (yes/no, cat/dog) — regression predicts continuous numeric values.
- ✗
Clustering
Why it's wrong here
Clustering groups similar items without predefined labels — regression predicts a specific numeric output.
- ✓
Regression
Why this is correct
Regression predicts continuous numeric values like prices, temperatures, or scores from input features.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Anomaly detection
Why it's wrong here
Anomaly detection identifies unusual data points — regression predicts continuous values for normal predictions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse regression with classification because both involve supervised learning, but regression outputs a continuous number while classification outputs a discrete label. Microsoft AI-900 tests this distinction by using examples like 'predicting temperature' versus 'predicting weather type'.
Trap categories for this question
Similar concept trap
Clustering groups similar items without predefined labels — regression predicts a specific numeric output.
Command / output trap
Clustering groups similar items without predefined labels — regression predicts a specific numeric output.
Detailed technical explanation
How to think about this question
Under the hood, regression models minimize a loss function such as mean squared error (MSE) to fit a line or hyperplane to the training data, with techniques like gradient descent optimizing the coefficients. In Azure Machine Learning, regression tasks can use algorithms like Fast Forest Quantile Regression or Poisson Regression, and the evaluation metrics include R-squared, root mean squared error (RMSE), and mean absolute error (MAE). A real-world scenario is predicting house prices based on features like square footage, number of bedrooms, 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 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.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe Artificial Intelligence workloads and considerations — This question tests Describe Artificial Intelligence workloads and considerations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Regression — Regression is the correct AI workload type because it is specifically designed to predict a continuous numeric value (e.g., price, temperature, sales amount) based on input features. Unlike classification, which predicts discrete categories, regression models output a real number by learning a mapping function from the input variables to a continuous target variable, often using algorithms like linear regression, decision trees, or neural networks.
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
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