AIF-C01 Question 102
Select 3A company wants to build an AI-powered application using a foundation model deployed on AWS. The application involves summarizing large amounts of legal documents and providing accurate insights to assist lawyers. Which design considerations should the company prioritize for this use case?
- A
Fine-tune the foundation model using a dataset of legal documents specific to the company's use case.
- B
Use a smaller pre-built model to reduce costs, even if it performs poorly with legal terminology.
- C
Ensure the foundation model is integrated with a robust data privacy and security framework to protect sensitive legal data.
- D
Leverage Amazon SageMaker's JumpStart to quickly deploy a pre-trained foundation model without assessing its suitability for legal language.
- E
Implement a feedback mechanism for users to flag inaccuracies in the model's summaries to improve future performance.
Show answer and explanation
Correct answers: A, C, E
Explanation
When designing applications with foundation models, it is crucial to tailor the model to the specific use case, especially in specialized fields like legal analysis. Fine-tuning ensures domain relevance, while robust security measures protect sensitive data. Feedback mechanisms also allow for iterative improvement of the model's performance. These considerations are vital to building a reliable and trustworthy AI-powered application.
- A. Correct.
Fine-tuning the foundation model using a dataset of legal documents ensures it is tailored to the domain-specific language and nuances of the legal field, which is critical for generating accurate and relevant summaries.
- B. Incorrect.
Using a smaller pre-built model may reduce costs, but if it cannot handle legal terminology effectively, it will fail to meet the application's functional requirements.
- C. Correct.
Protecting sensitive legal data is crucial in this use case. A robust data privacy and security framework ensures compliance with regulations and builds trust with users.
- D. Incorrect.
While Amazon SageMaker JumpStart can help deploy pre-trained models quickly, skipping the evaluation of the model's suitability for legal language could result in poor performance and irrelevant outputs.
- E. Correct.
Implementing a feedback mechanism allows the system to improve over time by learning from flagged inaccuracies, which is essential for maintaining the quality and reliability of the summaries.