AWS Certified AI Practitioner

Explanation

Correct Answer: B - Adopt RAG with Knowledge Bases to retrieve only relevant context at runtime

Why this is correct:

1. Cost Efficiency: RAG (Retrieval-Augmented Generation) with Knowledge Bases allows the model to retrieve only relevant information from external knowledge sources at runtime, rather than storing all data in the prompt or fine-tuning for every query.

2. Performance Maintenance: By retrieving only relevant context, the model can provide accurate responses without the overhead of processing entire medical records or unnecessary fine-tuning.

3. AWS Bedrock Integration: AWS Bedrock's Knowledge Bases feature is specifically designed for RAG implementations, allowing efficient retrieval of relevant information from large datasets.

Why other options are incorrect:

• A) Use fine-tuning for every patient query: This would be extremely costly and inefficient. Fine-tuning requires significant computational resources and time for each query, making it impractical for real-time applications.

• C) Store entire medical records in each prompt: This would lead to very large prompts, increasing token costs significantly and potentially hitting model context window limits. It's inefficient and expensive.

• D) Scale up GPU instances permanently: This would increase costs without addressing the root cause of inefficiency. Scaling resources permanently is not a cost-reduction strategy.

Key Benefits of RAG with Knowledge Bases:

• Reduces token usage by only including relevant information
• Maintains or improves accuracy by providing contextually relevant data
• Allows models to access up-to-date information without retraining
• More cost-effective than fine-tuning for every use case
• Scalable solution for handling large knowledge bases like medical records