Amazon AIF-C01 Practice Test - Questions Answers, Page 14

Which technique breaks a complex task into smaller subtasks that are sent sequentially to a large language model (LLM)?

A retail company wants to build an ML model to recommend products to customers. The company wants to build the model based on responsible practices. Which practice should the company apply when collecting data to decrease model bias?

Which option is a characteristic of AI governance frameworks for building trust and deploying human-centered AI technologies?

A company wants to enhance response quality for a large language model (LLM) for complex problem-solving tasks. The tasks require detailed reasoning and a step-by-step explanation process.

Which prompt engineering technique meets these requirements?

A media company wants to analyze viewer behavior and demographics to recommend personalized content. The company wants to deploy a customized ML model in its production environment. The company also wants to observe if the model quality drifts over time.

Which AWS service or feature meets these requirements?

A bank is fine-tuning a large language model (LLM) on Amazon Bedrock to assist customers with questions about their loans. The bank wants to ensure that the model does not reveal any private customer data.

Which solution meets these requirements?

An AI practitioner needs to improve the accuracy of a natural language generation model. The model uses rapidly changing inventory data.

Which technique will improve the model's accuracy?

HOTSPOT

A company is using Amazon SageMaker to develop AI models.

Select the correct SageMaker feature or resource from the following list for each step in the AI model lifecycle workflow. Each

SageMaker feature or resource should be selected one time or not at all. (Select TWO.)

SageMaker Clarify

SageMaker Model Registry

SageMaker Serverless Inference

A company is implementing intelligent agents to provide conversational search experiences for its customers. The company needs a database service that will support storage and queries of embeddings from a generative AI model as vectors in the database.

Which AWS service will meet these requirements?