1Foundation Model Integration, Data Management, and Compliance

• 1.1Requirements Analysis and Foundation Model Selection

  Learn to design GenAI solutions and select/configure the right foundation model (FM). Cover model evaluation in Amazon Bedrock, resilience via Cross-Region Inference, provisioned throughput, fine-tuning/LoRA, and the Well-Architected Framework (Generative AI Lens).

• 1.2Data Validation and Processing Pipelines for FM Consumption

  Learn pipelines that ensure quality of data fed to FMs: AWS Glue Data Quality, SageMaker Data Wrangler/Processing, multimodal processing (Transcribe, Textract), input formatting for the Bedrock API, and entity extraction with Comprehend.

• 1.3Designing Vector Store Solutions

  Learn vector database design for FM augmentation: Amazon Bedrock Knowledge Bases, OpenSearch Service (vector search/Neural plugin), Aurora pgvector, DynamoDB, metadata frameworks, and freshness via incremental updates.

• 1.4Retrieval Mechanisms for FM Augmentation (RAG)

  Learn mechanisms that improve RAG retrieval quality: chunking strategies, Titan embeddings, hybrid search, Bedrock reranker, query expansion/decomposition/transformation, and access via function calling/MCP.

• 1.5Prompt Engineering Strategies and Governance

  Learn prompt strategies that control FM behavior and the governance around them: Bedrock Prompt Management (templates/versions/variant comparison), Bedrock Guardrails, chain-of-thought, Bedrock Prompt Flows, and auditing via CloudTrail/CloudWatch Logs.