AWS / Professional / AIP-C01
Generative AI Developer Professional
Design production RAG and agent systems with controlled actions, tenant isolation, evaluation and measurable cost.
THE REVISION PATH
Your topics, in order.
Read. Recall. Explain the alternative.
Foundation Models and Generative AI
A model predicts plausible output; your application must establish whether it is useful and supported.
Prompt Engineering, RAG and Fine-Tuning
Prompt for instructions, retrieve for current evidence, tune for learned behaviour.
Agents, Tools and AWS AI Platforms
The model proposes; tools act; policy decides whether an action is allowed.
Evaluation and Responsible AI
Measure the answer, the experience and the harm; averages can hide who fails.
AI Security, Privacy and Governance
Protect the data path and the action path, then keep evidence of both.
Production Retrieval and Context Engineering
Retrieve evidence the caller may use, measure whether it is relevant, and fit it into the context deliberately.
Agent Integration, Inference APIs and Operations
Treat every model call and tool call as a bounded distributed-system operation.
GenAI Testing and Failure Diagnosis
Freeze the test conditions, separate failure stages and verify the final business action.
How this guide is organised
Original revision notes arranged around practical decisions. The linked official objectives define the mapped scope; primary documentation supports the explanations. Read each topic, answer without looking, then explain why another option would fail.
- Official exam guide ↗ Scope authority
Revision material supports preparation; it does not guarantee every possible exam question. Check the exam version and official objectives before booking.