You have a dataset that contains experimental data for fuel samples.

As described in the AI-900 study guide under “Identify features of machine learning,” regression is a supervised learning technique used to predict continuous numerical values. In this scenario, the goal is to predict energy output (a continuous variable) based on density (a numeric input).

Regression models find relationships between variables by fitting a line or curve that best represents the trend of the data. In Azure Machine Learning, regression algorithms such as linear regression, decision tree regression, and boosted decision trees are commonly used for such predictions.

Classification (A) predicts discrete labels (e.g., “High” or “Low”), not numeric values.

Clustering (B) groups similar data points but does not perform prediction.

Knowledge mining (C) extracts insights from unstructured data using tools like Azure AI Search and Cognitive Skills.

Hence, based on AI-900 fundamentals, predicting energy based on density requires a regression workload.