AWS Certified AI Practitioner AIF-C01 Practice Question
An ecommerce startup is building a customer-support chatbot on Amazon Bedrock. The bot must answer questions by using the company's product manuals stored in Amazon S3. To follow the Retrieval Augmented Generation (RAG) pattern, which additional component should the team add to the workflow?
Create a vector store such as an Amazon OpenSearch Service index that holds embeddings and returns relevant passages before invoking the model.
Configure an Amazon S3 Access Point so the model can read all manuals directly during inference.
Add an AWS Lambda function that sets the model's temperature to 0 for deterministic answers.
Run an Amazon SageMaker training job to fine-tune the foundation model on the S3 documents.
RAG solutions first retrieve content relevant to the user's question and then pass that content to a foundation model for generation. Amazon Bedrock supports this by connecting to a vector database where embeddings of the source documents are stored. When a query arrives, the vector store performs similarity search, returns the most relevant passages, and those passages are appended to the prompt that Bedrock sends to the model. Fine-tuning the model, granting the model blanket S3 access, or only adjusting inference parameters does not provide retrieval capabilities. Therefore, adding a vector store such as an Amazon OpenSearch Service index for embeddings is required to implement RAG.
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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