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

Many organizations rushed generative AI proofs of concept into production, and IT teams are now inheriting chatbots and pipelines that lack guardrails, monitoring, cost controls, and hardened APIs. For IT leaders, this course gives senior engineers a structured way to move from AI experiments to secure, supportable GenAI systems on AWS while preparing for the AIP-C01 Professional certification.

Role fit: Assign it to experienced developers, cloud engineers, and Team Leads who already work with AWS, APIs, and distributed applications. It is not an entry-level course. For engineers taking their first AWS exam, a lower-difficulty exam such as AWS Certified AI Practitioner can provide useful context for AWS question formats before tackling a professional-level certification.

Time investment: Plan for about 25 hours per learner across 25 skills. Learners can skip material they already know.

Risk and governance: The course covers GenAI guardrails, monitoring and compliance controls, API security, cost monitoring, and security and governance frameworks. It closes with an audit-and-harden exercise on an inherited chatbot, so teams practice fixing the kinds of systems they are likely to find in production.

Change management: Concepts are built by hand before introducing managed options like Amazon Bedrock Knowledge Bases, so engineers understand what those services do and what they leave out.

Hands-on builds in the AWS console and end-of-skill validation questions modeled on long, scenario-heavy professional exam items help teams build practical skill and exam readiness together.

Many organizations already run GenAI proofs of concept that were rushed into production. This course prepares IT teams to take those systems from fragile experiments to workloads that are secure, observable, and cost-aware. Engineers build a multimodal RAG system and an agentic application. The final project has them inherit a flawed production chatbot, GearBot, then audit it and harden it.

• Fewer hallucinations and unreliable answers: Your team learns to fix retrieval with hybrid BM25 and vector search, reranking, and query processing. They also learn to structure RAG prompts so the model uses and cites the context it receives.
• Safer autonomous agents: Engineers put circuit breakers, guardrails, and human-in-the-loop checkpoints in place. These controls stop runaway agent loops before they become an incident for the on-call engineer at 2 a.m.
• Cost-aware GenAI operations: Teams evaluate the operational tradeoffs that affect cost, reliability, and environmental impact when running large generative models. They practice choosing AWS services and architecture patterns that fit the workload instead of adopting GenAI because it’s trendy.
• Production-grade security and troubleshooting: Practitioners learn to publish model-backed APIs safely and to version models and prompts. They also learn to diagnose real failure modes, such as CloudWatch custom metrics that stop working after a Lambda function moves into a private VPC.

The result is an IT team that can choose the right AWS service for each requirement and work through scenario-heavy questions with distractors. Those same skills support the AIP-C01 exam and production decisions.

Knowledge

• How production RAG pipelines fit together: data enrichment with Amazon Bedrock, document chunking, embedding model selection, and vector stores built on pgvector and OpenSearch
• How retrieval choices affect RAG quality, and how query processing and reranking improve the results models receive
• How agentic AI works on AWS, including Bedrock Agents, Amazon Bedrock AgentCore memory, and Model Context Protocol (MCP) for decoupling agents from tool implementations
• Safety patterns that keep agents in check: circuit breakers, human-in-the-loop workflows, and guardrails
• Production concerns such as model and prompt versioning, batch inference, intelligent routing, and the trade-offs between provisioned throughput and on-demand capacity
• How to read long, professional-level AIP-C01 scenario questions, spot distractors, and choose among AWS services that overlap

Ability

• Design and build a multimodal RAG system, then structure prompts so the model uses and cites the context it retrieves instead of making things up
• Evaluate RAG output and recognize what managed options like Amazon Bedrock Knowledge Bases handle and what they leave out
• Build, test locally, and deploy agents with clear system prompts, scoped tools, and on-topic and off-topic examples that keep them focused
• Expose a GenAI model through a production API with security and access controls, and troubleshoot issues such as VPC endpoint and CloudWatch logging gaps
• Monitor GenAI workloads and tune them for cost and performance under variable, bursty demand
• Audit an inherited proof-of-concept chatbot, find its weaknesses, and harden it for production