A company is building a generative AI application to help customers make travel reservations. The application will process customer requests and invoke the appropriate API calls to complete reservation transactions.Which Amazon Bedrock resource will meet these requirements?
Answer(s): A
Agents in Amazon Bedrock are designed to interpret user requests, orchestrate workflows, and automatically invoke the appropriate APIs and services to complete tasks such as reservation transactions in a generative AIapplication.
A medical company deployed a disease detection model on Amazon Bedrock. To comply with privacy policies,the company wants to prevent the model from including personal patient information in its responses. The company also wants to receive notification when policy violations occur.Which solution meets these requirements?
Answer(s): C
Guardrails for Amazon Bedrock can be used to filter content and ensure that personal patient information is not included in model responses. Setting up Amazon CloudWatch alarms allows the company to receive notifications when policy violations occur. The other options are not specifically designed for filtering model output and monitoring policy compliance.
A medical company wants to develop an AI application that can access structured patient records, extract relevant information, and generate concise summaries.Which solution will meet these requirements?
Amazon Comprehend Medical is specifically designed to extract structured medical information (such as medication, condition, test results) from unstructured text. By applying rule-based logic afterward, relevant data can be formatted into concise summaries, meeting both the extraction and summarization needs.
A company is preparing for a compliance review. The company needs an AWS service that automatically collects information from the company's AWS accounts and generates reports for reviewers.Which AWS service meets these requirements?
Answer(s): B
AWS Audit Manager automatically collects evidence from AWS accounts, maps that evidence to compliance controls and frameworks, and generates audit-ready reports for reviewers.
What is tokenization used for in natural language processing (NLP)?
Tokenization is the process of dividing text into smaller units, such as words, subwords, or characters, which can then be analyzed or processed by NLP models. It is a foundational step in preparing text data for machine learning.
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