Supervised Learning — Microsoft Azure AI Fundamentals (AI-900) Practice Questions
Supervised learning is a machine learning approach in which a model is trained on a labeled dataset, meaning each training example includes both the input features and the correct output or target value the model should learn to predict. On the AI-900 exam, supervised learning is contrasted with unsupervised and reinforcement learning, and candidates are expected to recognize it as the paradigm behind common tasks such as classification, where the output is a category label, and regression, where the output is a continuous numeric value. Understanding that supervised learning requires labeled training data and produces a predictive model is foundational to answering questions about machine learning workload types on this exam. Azure Machine Learning supports supervised learning through automated ML, designer pipelines, and custom training jobs.
Free questions on supervised learning
More supervised learning questions in the full bank
- In machine learning, what is a "label"? Unlock answer & explanation →
- What is the main purpose of a training dataset in machine learning? Unlock answer & explanation →
- Which type of machine learning uses labeled data to train the model? Unlock answer & explanation →