Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 2 discussion

The law firm wants to extract key points from legal documents, which aligns with the goal of summarization. A summarization chatbot powered by large language models (LLMs) can read through legal documents and provide concise, accurate summaries that capture the essential points, making it the most appropriate choice.

"C" is correct - The primary requirement is to read legal documents and extract key points. Summarization is the best approach for condensing lengthy legal text into key points while preserving important details. "A" is incorrect - NER helps identify names, dates, contract numbers. but does not summarize key points from documents.

A - Wrong - extract predefined entities like people, place, org etc. C - extract summary.

I guess, C is correct answer because question talks about generating key-points or kind of a summary of important points from the document.

AWS certification exams are introducing new question types, including ordering, matching, and case study questions, alongside traditional multiple choice and multiple response formats. The ordering type requires arranging selected responses in the correct sequence, while matching questions involve linking statements to prompts. Case studies recycle a scenario across multiple questions, allowing candidates to save time by understanding the context once. Each question is evaluated independently, meaning it's crucial to answer all parts correctly to receive credit.

NER should be more suitable for the legal documents. It is recommended by the Amazon Comprehend docs. When you try to ask an AI Assistant without giving them answers, it will also prefer NER with its advantageous.

Answer:C. Develop a summarization chatbot

just refer to Amazon comprehend docs, it is designed to do this type of task.

Answer: C. Develop a summarization chatbot.

C: Develop a summarization chatbot. Explanation: A summarization chatbot powered by large language models (LLMs) can read and analyze legal documents to extract key points. This aligns with the law firm’s requirement to process complex documents and provide concise summaries of the critical information.

Named entity recognition (NER)—also called entity chunking or entity extraction—is a component of natural language processing (NLP) that identifies predefined categories of objects in a body of text. These categories can include, but are not limited to, names of individuals, organizations, locations, expressions of times, quantities, medical codes, monetary values and percentages, among others. Essentially, NER is the process of taking a string of text (i.e., a sentence, paragraph or entire document), and identifying and classifying the entities that refer to each category.

“... extract key points from the documents." means summarization task.

NER is a feature of Amazon Comprehend specifically designed for this type of tasks

C. Develop a summarization chatbot. Explanation: A summarization chatbot can leverage large language models (LLMs) to automatically read and extract key points from legal documents by summarizing the content. This approach aligns well with the firm's need to condense lengthy documents into concise, relevant summaries, making it easier for users to quickly understand the main points without reading the entire document. LLMs are highly effective at summarization tasks, especially when fine-tuned on domain-specific data like legal text.

Building an AI-powered web application with document summarization and chatbot features can significantly enhance user experience by providing quick, relevant insights and interactive support