Free Practice Amazon AIF-C01 Exam Questions 2025

A financial services company is exploring the use of generative AI to automate report generation and enhance customer insights. As part of this effort, the team is evaluating different AI models and technologies, including Foundation Models (FMs), to understand their capabilities and how they can be applied to improve efficiency. The team needs to clarify key characteristics and features of Foundation Models to determine if they align with the company's objectives.Which of the following statements is correct regarding Foundation Models (FMs) in the context of generative AI?

A retail company is looking to streamline its machine learning workflows and improve collaboration between data science teams. The team is considering using MLflow with Amazon SageMaker to manage the end-to-end machine learning lifecycle. Understanding how MLflow integrates with Amazon SageMaker will help the team decide if this combination is the right fit for their machine learning project management needs.Given this context, which statement best defines the use of MLflow with Amazon SageMaker?

A company has implemented a chatbot powered by Amazon Bedrock to handle customer inquiries and support requests. While the chatbot is effective at providing automated responses, the company has noticed that some of the replies do not consistently match its desired tone — professional, empathetic, and friendly. To maintain brand consistency and ensure a positive customer experience, the company needs to align the chatbot’s responses with its specific communication style and standards.Which approach would be most effective for ensuring that the chatbot's responses are consistently aligned with the company's tone and style?

A company is using Amazon Bedrock and it wants to set an upper limit on the number of tokens returned in the model's response.

Which of the following inference parameters would you recommend for the given use case?

A retail company is using an Amazon SageMaker machine learning model to analyze customer purchasing patterns and predict future buying behavior. The data scientists at the company regularly need to process datasets of less than 1 GB, such as daily sales records and customer interaction logs. The company does not require immediate responses and can afford some delay in receiving the analysis results, as the insights are primarily used for weekly strategy meetings.Which inference method would be the most suitable for the company in this scenario?