AWS Certified Generative AI Developer - Professional
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A book publishing company wants to build a book recommendation system that uses an AI assistant. The AI assistant will use ML to generate a list of recommended books from the company's book catalog. The system must suggest books based on conversations with customers.
The company stores the text of the books, customers' and editors' reviews of the books, and extracted book metadata in Amazon S3. The system must support low-latency responses and scale efficiently to handle more than 10,000 concurrent users.
Which solution will meet these requirements?
A
Use Amazon Bedrock Knowledge Bases to generate embeddings. Store the embeddings as a vector store in Amazon OpenSearch Service. Create an AWS Lambda function that queries the knowledge base. Configure Amazon API Gateway to invoke the Lambda function when handling user requests.
B
Use Amazon Bedrock Knowledge Bases to generate embeddings. Store the embeddings as a vector store in Amazon DynamoDB. Create an AWS Lambda function that queries the knowledge base. Configure Amazon API Gateway to invoke the Lambda function when handling user requests.
C
Use Amazon SageMaker AI to deploy a pre-trained model to build a personalized recommendation engine for books. Deploy the model as a SageMaker AI endpoint. Invoke the model endpoint by using Amazon API Gateway.
D
Create an Amazon Kendra GenAI Enterprise Edition index that uses the S3 connector to index the book catalog data stored in Amazon S3. Configure built-in FAQ in the Kendra index. Develop an AWS Lambda function that queries the Kendra index based on user conversations. Deploy Amazon API Gateway to expose this functionality and invoke the Lambda function.
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