Question 43 - D-GAI-F-01 discussion

When a machine learning engineer is training a model using labeled data, the type of learning being employed is supervised learning. In supervised learning, the model is trained on a labeled dataset, which means that each training example is paired with an output label. The model learns to predict the output from the input data, and the goal is to minimize the difference between the predicted and actual outputs.

The Official Dell GenAI Foundations Achievement document likely covers the fundamental concepts of machine learning, including supervised learning, as it is one of the primary categories of machine learning. It would explain that supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs12. The data is known as training data, and it consists of a set of training examples. Each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). The supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.

Self-supervised learning (Option OA) is a type of unsupervised learning where the system learns to predict part of its input from other parts. Unsupervised learning (Option OB) involves training a model on data that does not have labeled responses. Reinforcement learning (Option OD) is a type of learning where an agent learns to make decisions by performing actions and receiving rewards or penalties. Therefore, the correct answer is C. Supervised learning, as it directly involves the use of labeled data for training models.