Question 2
Domain 5: Security, Compliance, and Governance for AI SolutionsA law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents. Which solution meets these requirements?
Correct answer: C
Explanation
Summarization is the listed GenAI use case for condensing long documents: “Legal departments summarize contracts to highlight key obligations.” A chatbot built on LLMs can read legal documents and extract key points by generating a concise summary from the source text.
Why each option is right or wrong
A. Build an automatic named entity recognition system.
Named entity recognition extracts entities, not document-level key points or summaries.
B. Create a recommendation engine.
Recommendation engines suggest items based on behavior or preferences, not document extraction.
C. Develop a summarization chatbot.
The requirement is to read source documents and extract the key points, which aligns with the GenAI use case of summarization in Table 2.1.1, where long documents are condensed into shorter versions. On AWS, this is typically implemented with an LLM-based application such as Amazon Bedrock, which provides managed foundation-model APIs without needing to provision GPU infrastructure, so a summarization chatbot is the correct fit for document-to-summary extraction.
D. Develop a multi-language translation system.
Translation converts text between languages; it does not extract key points from legal documents.