AWS Certified AI Practitioner Practice Exam

Which learning strategy allows a customer service chatbot to improve its responses based on past interactions?

1. Supervised learning with a manually curated dataset of good response and bad responses

2. Reinforcement learning with rewards for positive customer feedback

3. Unsupervised learning to find clusters of similar customer inquiries

4. Supervised learning with a continuously updated FAQ dataset

The correct answer is: Reinforcement learning with rewards for positive customer feedback

The most fitting learning strategy for enabling a customer service chatbot to enhance its responses based on prior interactions is reinforcement learning. This approach centers on learning through trial and error, where the chatbot receives feedback in the form of rewards or penalties based on the outcomes of its responses. In the context of a customer service chatbot, positive customer feedback serves as a reward, encouraging the chatbot to replicate successful interaction patterns. Conversely, negative feedback would function as a penalty, prompting the model to adjust and refine its responses to avoid similar mistakes in the future. This dynamic process allows the chatbot to learn from its experiences over time, leading to improved performance and more effective interactions with users. In contrast, while supervised learning can also be useful, it typically involves training on a fixed dataset and may not have the same flexibility or adaptability as reinforcement learning when it comes to ongoing improvements based on real-time feedback and interaction history. This makes reinforcement learning the superior choice in this scenario for continuous improvement based on past interactions.