Questão 26 — Amazon AIF-C01 Simulado | AWS Certified AI Practitioner Questões e Respostas

Explanation: The company wants to improve the accuracy of a generative AI application using a foundation model (FM) on Amazon Bedrock in the most cost-effective way. Prompt engineering involves optimizing the input prompts to guide the FM to produce more accurate responses without modifying the model itself. This approach is cost-effective because it does not require additional computational resources or training, unlike fine-tuning or retraining. Exact Extract from AWS AI Documents: From the AWS Bedrock User Guide: "Prompt engineering is a cost-effective technique to improve the performance of foundation models. By crafting precise and context-rich prompts, users can guide the model to generate more accurate and relevant responses without the need for fine-tuning or retraining." (Source: AWS Bedrock User Guide, Prompt Engineering for Foundation Models) Detailed Option A: Fine-tune the FM.Fine-tuning involves retraining the FM on a custom dataset, which requirescomputational resources, time, and cost (e.g., for Amazon Bedrock fine-tuning jobs). It is not the most cost-effective solution. Option B: Retrain the FM.Retraining an FM from scratch is highly resource-intensive and expensive, as it requires large datasets and significant compute power. This is not cost-effective. Option C: Train a new FM.Training a new FM is the most expensive option, as it involves building a model from the ground up, requiring extensive data, compute resources, and expertise. This is not cost-effective. Option D: Use prompt engineering.This is the correct answer. Prompt engineering adjusts the input prompts to improve the FM's responses without incurring additional compute costs, making it the most cost- effective solution for improving accuracy on Amazon Bedrock. Reference: AWS Bedrock User Guide: Prompt Engineering for Foundation Models (https://docs.aws.amazon.com/ bedrock/latest/userguide/prompt-engineering.html) AWS AI Practitioner Learning Path: Module on Generative AI Optimization Amazon Bedrock Developer Guide: Cost Optimization for Generative AI (https://aws.amazon.com/bedrock/)