AWS Certified Cloud Practitioner
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A university research lab stores large collections of academic papers in Amazon S3 and wants to make them searchable via a Bedrock chatbot. Which approach provides this functionality?
Real Exam
Community
RRitesh
A
Ground Truth + Bedrock
B
Knowledge Bases for Amazon Bedrock (RAG)
C
Bedrock Fine-Tuning
D
Bedrock Guardrails
Explanation:
Explanation
Knowledge Bases for Amazon Bedrock (RAG) is the correct approach because:
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RAG (Retrieval-Augmented Generation) is specifically designed to make external data sources searchable by AI models
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Knowledge Bases can connect to Amazon S3 to index and retrieve documents
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It enables the chatbot to search through large collections of academic papers stored in S3
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Provides semantic search capabilities across the document repository
Why other options are incorrect:
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A) Ground Truth + Bedrock: Ground Truth is for creating labeled datasets for machine learning, not for making existing documents searchable
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C) Bedrock Fine-Tuning: Fine-tuning trains models on specific data but doesn't provide search functionality across document collections
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D) Bedrock Guardrails: Guardrails are for content safety and filtering, not for document search capabilities
Knowledge Bases with RAG is the AWS service specifically designed for this use case of making S3-stored documents searchable through AI chatbots.
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