A security company is evaluating Amazon Rekognition to enhance its Machine Learning (ML) capabilities. However, the data science team needs to identify scenarios where Amazon Rekognition may not be the most suitable solution. Understanding these limitations will help the team select the right tools for different aspects of their security system.Given this context, which of the following use cases is NOT the right fit for Amazon Rekognition?

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 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 marketing company is researching generative AI technologies to better understand how they work and what makes them suitable for automating creative tasks. Understanding the core principles of generative AI will help the company determine if it’s the right fit for their content creation needs.Given this context, which of the following best describes generative AI?

Which statement best defines the use of MLflow with Amazon SageMaker ?