A company uses a foundation model from Amazon Bedrock and wants to improve its accuracy using the company's own data. Which approach correctly describes how to fine-tune the model?

Fine-tuning a foundation model on Amazon Bedrock requires a labeled dataset in a specific format. The correct approach is to provide a dataset where each entry contains a "prompt" (the input you'd give the model) and a corresponding "completion" (the desired output from the model). This allows the model to learn the specific patterns and responses relevant to your data. Preparing data as a .txt file with CSV formatting is incorrect because it doesn't specify the required prompt/completion structure. Purchasing Provisioned Throughput is for reserving model capacity, not for fine-tuning. Training with journals and textbooks describes pre-training, not fine-tuning with specific company data.