Banco de preguntas (422)

An airline company wants to build a conversational AI assistant to answer customer questions about flight schedules, booking, and payments. The company wants to use large language models (LLMs) and a knowledge base to create a text-based chatbot interface. Which solution will meet these requirements with the LEAST development effort?

What is tokenization used for in natural language processing (NLP)?

Which option is a characteristic of transformer-based language models?

A financial company is using AI systems to obtain customer credit scores as part of the loan application process. The company wants to expand to a new market in a different geographic area. The company must ensure that it can operate in that geographic area. Which compliance laws should the company review?

A company uses Amazon Bedrock for its generative AI application. The company wants to use Amazon Bedrock Guardrails to detect and filter harmful user inputs and model-generated outputs. Which content categories can the guardrails filter? (Choose two.)

Which scenario describes a potential risk and limitation of prompt engineering in the context of a generative AI model?

A publishing company built a Retrieval Augmented Generation (RAG) based solution to give its users the ability to interact with published content. New content is published daily. The company wants to provide a near real-time experience to users. Which steps in the RAG pipeline should the company implement by using offline batch processing to meet these requirements? (Choose two.)

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

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?

A company wants to collaborate with several research institutes to develop an AI model. The company needs standardized documentation of model version tracking and a record of model development. Which solution meets these requirements?

A company that uses multiple ML models wants to identify changes in original model quality so that the company can resolve any issues. Which AWS service or feature meets these requirements?

What is the purpose of chunking in Retrieval Augmented Generation (RAG)?

A company is developing an editorial assistant application that uses generative AI. During the pilot phase, usage is low and application performance is not a concern. The company cannot predict application usage after the application is fully deployed and wants to minimize application costs. Which solution will meet these requirements?

A company deployed a Retrieval Augmented Generation (RAG) application on Amazon Bedrock that gathers financial news to distribute in daily newsletters. Users have recently reported politically influenced ideas in the newsletters. Which Amazon Bedrock guardrail can identify and filter this content?

A financial company is developing a fraud detection system that flags potential fraud cases in credit card transactions. Employees will evaluate the flagged fraud cases. The company wants to minimize the amount of time the employees spend reviewing flagged fraud cases that are not actually fraudulent. Which evaluation metric meets these requirements?

A company designed an AI-powered agent to answer customer inquiries based on product manuals. Which strategy can improve customer confidence levels in the AI-powered agent's responses?

A hospital developed an AI system to provide personalized treatment recommendations for patients. The AI system must provide the rationale behind the recommendations and make the insights accessible to doctors and patients. Which human-centered design principle does this scenario present?

Which statement presents an advantage of using Retrieval Augmented Generation (RAG) for natural language processing (NLP) tasks?

A company has created a custom model by fine-tuning an existing large language model (LLM) from Amazon Bedrock. The company wants to deploy the model to production and use the model to handle a steady rate of requests each minute. Which solution meets these requirements MOST cost-effectively?

Which technique involves training AI models on labeled datasets to adapt the models to specific industry terminology and requirements?