This section of the AI-900: Microsoft Azure AI Fundamentals exam focuses on understanding one of the most important foundational concepts in machine learning: features and labels. You are not expected to build models or write code, but you must be able to recognize features and labels in a dataset and understand their role in different machine learning scenarios.
This topic appears under: Describe Artificial Intelligence workloads and considerations (15–20%) → Describe core machine learning concepts
What Is a Dataset in Machine Learning?
A dataset is a collection of data used to train, validate, and test machine learning models. In supervised learning scenarios (which are emphasized in AI-900), a dataset typically contains:
- Features: The input values used to make predictions
- Labels: The output or target values the model learns to predict
Each row in a dataset usually represents a single observation or record, and each column represents either a feature or a label.
What Are Features?
Features are the individual measurable properties or characteristics of the data that are used as inputs to a machine learning model.
Key Characteristics of Features
- Features describe what you know about each data point
- They are used by the model to identify patterns
- Features can be numerical, categorical, or derived
Examples of Features
| Scenario | Example Features |
|---|---|
| House price prediction | Number of bedrooms, square footage, location |
| Customer churn | Account age, number of support tickets, monthly spend |
| Email classification | Word frequency, sender domain, message length |
In Azure Machine Learning, features are often referred to as input variables.
What Are Labels?
A label is the value that a machine learning model is trained to predict. Labels are only present in supervised learning datasets.
Key Characteristics of Labels
- Labels represent the outcome or answer
- A dataset usually has one label column
- Labels are known during training but unknown during prediction
Examples of Labels
| Scenario | Label |
| House price prediction | Sale price |
| Customer churn | Churned (Yes/No) |
| Image classification | Object category |
In Azure Machine Learning, labels are often called target variables.
Features vs Labels: Key Differences
| Aspect | Features | Labels |
| Purpose | Input to the model | Output to predict |
| Quantity | Usually many | Typically one |
| Known during training | Yes | Yes |
| Known during prediction | Yes | No |
Understanding this distinction is critical for AI-900 exam questions.
Features and Labels in Supervised Learning
Supervised learning relies on labeled datasets. The model learns by comparing its predictions to the known labels and adjusting accordingly.
Common Supervised Learning Types
- Regression
- Features: numeric or categorical inputs
- Label: numeric value (e.g., price, temperature)
- Classification
- Features: descriptive inputs
- Label: category or class (e.g., spam/not spam)
Features and Labels in Unsupervised Learning
Unsupervised learning datasets do not contain labels.
- The model identifies patterns or groupings on its own
- Common example: clustering
In AI-900, this distinction is important:
If a dataset has no labels, it is not supervised learning.
Real-World Azure Example
Consider a dataset used in Azure Machine Learning to predict whether a customer will cancel a subscription.
- Features:
- Number of logins per month
- Subscription length
- Customer support interactions
- Label:
- Subscription canceled (Yes or No)
The model learns the relationship between the features and the label to make future predictions.
Exam Tips for AI-900
- If the question asks “what the model uses to make predictions”, look for features
- If the question asks “what the model predicts”, look for labels
- If labels are present, it is supervised learning
- AI-900 focuses on conceptual understanding, not data science implementation
Key Takeaways
- Features are input variables used to make predictions
- Labels are the known outcomes the model learns to predict
- Supervised learning requires labeled data
- Being able to identify features and labels in a scenario is essential for AI-900
This knowledge forms the foundation for understanding regression, classification, and many Azure AI workloads covered later in the exam.
Go to the Practice Exam Questions for this topic.
Go to the AI-900 Exam Prep Hub main page.
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