FM Integration & Data

Design, models, vectors, retrieval, prompts — 31% of the exam

• The AIP Exam: Blueprint, Logistics, Strategy — What the AWS Certified Generative AI Developer – Professional credential is, its five-domain blueprint, how the professional bar differs from associate exams, and how to ride this path to a 750. (4 lessons, 45 min)
• Designing GenAI Solutions and Selecting Models — Requirements to architecture: proofs of concept on Bedrock, the Well-Architected Generative AI Lens, model selection by benchmark and capability, provider-switch and cross-Region resilience, and the customization lifecycle. (4 lessons, 60 min)
• Data Pipelines for FM Consumption — Validating and processing data on its way into foundation models: Glue Data Quality and Data Wrangler gates, multimodal pipelines for text, image, audio and tabular data, and model-specific input formatting. (3 lessons, 55 min)
• Vector Stores at Professional Scale — Choosing and operating the vector layer: Bedrock Knowledge Bases, OpenSearch neural search, Aurora and S3 options, metadata frameworks, sharding and hierarchical indexing, and the sync pipelines that keep indexes honest. (3 lessons, 60 min)
• Retrieval Engineering — The retrieval half of RAG at professional depth: chunking strategies including hierarchical, embedding model selection, hybrid search with rerankers, query expansion and decomposition, and function-calling retrieval through MCP. (4 lessons, 60 min)
• Prompt Engineering and Governance — Prompts as governed artifacts: Bedrock Prompt Management templates and approval workflows, parameterized role definitions, conversation history design, prompt regression testing, chain-of-thought patterns, and Prompt Flows. (3 lessons, 55 min)