The organization’s online retail system popularity has resulted in global demand.

The scenario describes a virtual assistant designed to provide simple instructions for product returns based onpre-defined questionscommonly asked by customers. This indicates the use ofsupervised machine learning(B), where the system is trained on a labeled dataset (e.g., questions paired with correct responses) to predict appropriate answers. Supervised learning is ideal for applications like chatbots or virtual assistants that rely on predefined input-output pairs to handle customer queries efficiently.

Unsupervised (A):Involves finding patterns in unlabeled data (e.g., clustering), not suitable for predefined question-response tasks.

Reinforcement learning (C):Focuses on learning through trial and error with rewards, used in dynamic environments (e.g., robotics), not for static question answering.

Deep learning (D):A subset of supervised or unsupervised learning using neural networks, but the question doesn’t specify complex architectures, making supervised learning the broader, correct choice.

Supervised learning aligns withIT strategyfor deploying AI-driven customer support tools, as it ensures accurate, predictable responses based on trained data, enhancing user experience in a global retail system.