Free Amazon AIF-C01 Exam Questions

A retail company is exploring the use of generative AI to improve its customer experience by personalizing recommendations and automating product descriptions. The company is interested in leveraging pre-built, high-performing Foundation Models (FMs) to accelerate development but also needs the ability to customize these models with its proprietary data to meet specific business needs. The company is seeking an AWS service that not only provides access to a variety of Foundation Models but also offers the option to privately fine-tune and customize these models for their unique use case.Which AWS service/feature offers a choice of high-performing Foundation Models (FMs) and the ability to privately customize the FMs with your data?

An AI paritioner is developing a new ML model. After training the model, the AI practitionerevaluates the accuracy of the model's predictions. The model's accuracy is low when the model usesboth the training dataset and the test dataset.Which scenario is the MOST likely cause of this problem? 

Which AWS service is specifically designed for converting medical speech to text, ensuring compliance with healthcare regulations such as HIPAA?

A machine learning team at a tech company is developing a generative AI model to automate text generation for customer support. As part of optimizing the model’s performance, the team needs to adjust both model parameters and hyperparameters but wants to clearly understand the distinctions between the two. Understanding these differences is crucial for fine-tuning the model and improving its output.Which of the following highlights the key differences between model parameters and hyperparameters in the context of generative AI?

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?