A company is uploading thousands of PDF policy documents into Amazon S3 and Amazon Bedrock Knowledge Bases. Each document contains structured sections. Users often search for a small section but need the full section context. The company wants accurate section-level search with automatic context retrieval and minimal custom coding.
Which chunking strategy meets these requirements?
A company runs an Amazon SageMaker AI domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker AI domain.
Recently, the company discovered suspicious traffic to the domain from a specific IP address. The company needs to block traffic from the specific IP address.
Which update to the network configuration will meet this requirement?
A company is building a conversational AI assistant on Amazon Bedrock. The company is using Retrieval Augmented Generation (RAG) to reference the company's internal knowledge base. The AI assistant uses the Anthropic Claude 4 foundation model (FM).
The company needs a solution that uses a vector embedding model, a vector store, and a vector search algorithm.
Which solution will develop the AI assistant with the LEAST development effort?
An ML engineer is developing a fraud detection model by using the Amazon SageMaker XGBoost algorithm.
The model classifies transactions as either fraudulent or legitimate.
During testing, the model excels at identifying fraud in the training dataset. However, the model is inefficient at identifying fraud in new and unseen transactions.
What should the ML engineer do to improve the fraud detection for new transactions?
A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker compute costs reach a specific threshold.
Which solution will meet these requirements?
