Foundation Model Integration, Data Management, and Compliance — AWS Certified Generative AI Developer - Professional

Official references for this objective

• Amazon Web Services — Build Generative AI Applications with Foundation Models - Amazon Bedrock FAQs - AWS

  your content is not used to improve the base models and is not shared with any model providers

• Amazon Web Services — Simplified model access in Amazon Bedrock | AWS Security Blog

  IAM statements with the PutFoundationModelEntitlement permission no longer have an effect.

• Amazon Web Services — Submit a model customization job for fine-tuning - Amazon Bedrock

  If you don't specify the customizationType , the model customization method defaults to FINE_TUNING .

• Amazon Web Services — Data protection - Amazon Bedrock

  You are responsible for maintaining control over your content that is hosted on this infrastructure. You are also responsible for the security configuration and management

• Amazon Web Services — Sync your data with your Amazon Bedrock knowledge base - Amazon Bedrock

  Only metadata.json files are modified. No content files are changed. The associated content files are not CSV files. The data source does not use a

• Amazon Web Services — Customize your model to improve its performance for your use case - Amazon Bedrock

  Instead of providing labeled input-output pairs, you define reward functions that evaluate response quality. The model learns iteratively by receiving feedback scores from these reward

• Amazon Web Services — Ingest changes directly into a knowledge base - Amazon Bedrock

  For S3 data sources, any changes that you index into the knowledge base directly in the AWS Management Console or with the KnowledgeBaseDocuments API operations

• Amazon Web Services — Model sources and license agreements - Amazon SageMaker AI

  The accept_eula value is None by default and must be explicitly redefined as True in order to accept the end-user license agreement.

• Amazon Web Services — Overview - Amazon Bedrock

  Converse API import boto3 client = boto3.client('bedrock-runtime', region_name='us-east-1') response = client.converse( modelId='anthropic.claude-opus-4-7', messages=[