AIF-C01 Question 101
Select 3Your team is building a customer support chatbot using a foundation model like Amazon Bedrock. The chatbot will handle sensitive customer data, including personal identification and payment information. What key design considerations should you focus on to ensure a secure and effective application?
- A
Ensure proper fine-tuning of the foundation model for your specific domain to improve response relevance.
- B
Implement encryption mechanisms to secure sensitive customer data during storage and transmission.
- C
Configure the foundation model to directly store customer data for future training improvements.
- D
Use access controls and role-based permissions to prevent unauthorized use of the model and data.
- E
Avoid pre-training the foundation model to save costs, even if domain-specific data is available.
Show answer and explanation
Correct answers: A, B, D
Explanation
When designing applications using foundation models, especially for use cases involving sensitive data, it is essential to focus on security and customization. Fine-tuning ensures the model is relevant to the specific use case. Encryption protects sensitive data in storage and transit, and access controls prevent unauthorized access. Avoiding these design considerations could lead to security vulnerabilities, regulatory non-compliance, and poor application performance.
- A. Correct.
Fine-tuning a foundation model for your specific domain ensures the chatbot produces relevant and accurate responses tailored to your use case, improving user satisfaction and effectiveness.
- B. Correct.
Implementing encryption mechanisms is critical for protecting sensitive customer data, ensuring compliance with data security regulations and maintaining user trust.
- C. Incorrect.
Foundation models should not directly store customer data, as this poses significant security and privacy risks. Instead, data should be handled transiently and securely.
- D. Correct.
Access controls and role-based permissions help prevent unauthorized access to both the model and sensitive data, minimizing the risk of data breaches and misuse.
- E. Incorrect.
Avoiding pre-training is not a best practice when domain-specific data is available. Pre-training or fine-tuning can improve the performance and relevance of the foundation model for the specific application.