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.