AWS Certified Generative AI Developer - Professional (AIP-C01)

Generative AI pilots create new operational risks when teams move into production without shared patterns for retrieval, agents, security, monitoring, and auditability. IT Directors can assign this expert-level AWS course to experienced cloud developers, AI engineers, platform teams, and senior IT Practitioners responsible for building or governing GenAI applications on AWS. The course is a realistic ~25-hour per-learner investment, best delivered as a structured enablement path rather than ad hoc exploration.

The curriculum moves from GenAI architecture and Amazon Bedrock through RAG design, vector stores, hybrid search, agentic AI, guardrails, versioning, cost optimization, API security, and application hardening. For change management, Team Leads can use it to establish common vocabulary and implementation patterns before approving production GenAI work. CBT Nuggets Playlists and Team Reporting help Training Managers assign the path, monitor completion, and validate readiness across the team.

IT teams complete this training to reduce the gap between GenAI experimentation and production-ready AWS implementation. The course focuses on practical design areas that commonly affect reliability, cost, and governance in enterprise GenAI systems.

• Build stronger RAG foundations with document chunking, embedding selection, vector databases, OpenSearch setup, BM25, hybrid search, query processing, reranking, and prompt engineering.
• Standardize Amazon Bedrock usage across Knowledge Bases, agents, AgentCore, guardrails, batch inference, and intelligent routing.
• Improve production readiness with model and prompt versioning, operational monitoring, cost optimization, production API design, security, auditing, and hardening.
• Support safer agentic AI adoption with multi-agent orchestration, Model Context Protocol integration, circuit breaker patterns, and human-in-the-loop workflows.

Knowledge

• Core architecture patterns for AWS-based generative AI solutions.
• How RAG systems use enrichment, chunking, embeddings, vector stores, hybrid search, reranking, and evaluation.
• Amazon Bedrock capabilities, including Knowledge Bases, agents, AgentCore, guardrails, and batch inference.
• Agentic AI concepts, including prompt engineering for agents, multi-agent orchestration, MCP integration, circuit breakers, and human-in-the-loop patterns.
• Production concerns for GenAI applications, including versioning, monitoring, cost optimization, API security, auditing, and hardening.

Ability

• Evaluate model, embedding, and vector storage choices for AWS GenAI workloads.
• Design RAG workflows that retrieve, rank, and present context more effectively.
• Apply prompt engineering patterns for both RAG and agentic AI use cases.
• Plan operational controls for monitoring, cost management, and intelligent routing.
• Support production deployment decisions with security, guardrail, audit, and hardening practices.