AWS Certified AI Practitioner (AIF-C01) — Question 120

A bank has fine-tuned a large language model (LLM) to expedite the loan approval process. During an external audit of the model, the company discovered that the model was approving loans at a faster pace for a specific demographic than for other demographics.

How should the bank fix this issue MOST cost-effectively?

Answer options

• A. Include more diverse training data. Fine-tune the model again by using the new data.
• B. Use Retrieval Augmented Generation (RAG) with the fine-tuned model.
• C. Use AWS Trusted Advisor checks to eliminate bias.
• D. Pre-train a new LLM with more diverse training data.

Correct answer: A

Explanation

The correct answer is A because including more diverse training data directly addresses the bias in the model and allows for cost-effective fine-tuning without starting over. Option B, while useful, does not specifically target the bias issue. Option C is not the most effective solution as it focuses on checks rather than direct intervention, and Option D is more costly and time-consuming than simply fine-tuning the existing model.