AWS Certified AI Practitioner AIF-C01 Practice Question
A company is building a customer support chatbot on Amazon Bedrock. The bot must answer questions using the company's 1 TB product manual stored in Amazon S3, but the team wants to avoid the cost of model fine-tuning. Which approach best meets the requirement?
Pre-train a new foundation model from scratch by using the product manuals as the training corpus before deploying it in Bedrock.
Fine-tune the selected foundation model in Bedrock by uploading the manuals so the information is permanently written into the model weights.
Configure an Amazon Bedrock knowledge base to retrieve relevant passages at runtime and inject them into the prompt (Retrieval Augmented Generation).
Increase the model's temperature parameter so it can generate answers that reference the manuals without needing additional data sources.
Using Retrieval Augmented Generation (RAG) with an Amazon Bedrock knowledge base allows the chatbot to retrieve only the passages that are relevant to each user query and insert them into the prompt sent to the foundation model. Because the content remains in an external vector store, the foundation model itself does not need to be fine-tuned or retrained, saving both time and cost while still providing accurate, up-to-date answers. Pre-training a new model or full fine-tuning would be far more expensive and time-consuming. Simply increasing the temperature changes randomness, not knowledge, so it would not enable the model to reference proprietary manuals.
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ELI5: What is Retrieval Augmented Generation (RAG)?
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What is an Amazon Bedrock knowledge base and how does it work?
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Why is fine-tuning or pre-training models more expensive than using RAG?
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What is Retrieval Augmented Generation (RAG)?
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Why is fine-tuning a model considered expensive and time-consuming?
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What role does Amazon S3 play in the Retrieval Augmented Generation process?
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AWS Certified AI Practitioner AIF-C01
Applications of Foundation Models
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