200+ AWS AI Practitioner Exam Questions

Question

A global e-commerce company wants to automatically classify millions of incoming customer support emails into categories such as “refund request,” “delivery issue,” and “technical problem.” The company also wants the system to improve over time as new types of queries emerge. They have labeled historical data but no strict requirement for human-interpretable decision rules.

Which AI/ML approach best fits this requirement?

A. Rule-based keyword matching system
B. Supervised learning classification model
C. Unsupervised clustering model without labels
D. Deterministic workflow automation using if-else logic

Correct Answer: B

Explanation:
Supervised learning classification is the most appropriate approach because the company already has labeled historical data that maps emails to predefined categories. This allows a model to learn patterns from input text and accurately predict categories for new, unseen emails. Classification models are designed specifically for assigning discrete labels, making them ideal for tasks like routing support tickets, spam detection, or intent recognition. Over time, the model can be retrained with new labeled data, allowing it to adapt to emerging patterns in customer queries, which aligns with the requirement for continuous improvement.

This approach also scales efficiently for millions of inputs, which is critical in high-volume environments like global e-commerce support systems. Unlike rule-based or deterministic systems, supervised models generalize better to variations in language and phrasing. AWS services like Amazon Comprehend or Amazon SageMaker can be used to build and deploy such classification models. These services support training custom classifiers and handling large-scale inference workloads, making them suitable for production-grade implementations where accuracy, scalability, and adaptability are required.