AWS Generative AI Developer – Professional Prerequisites (AIP-C01): What You Need First

Does AIP-C01 Have Prerequisites?

No — there are no required prerequisite exams for the AWS Certified Generative AI Developer – Professional (AIP-C01) exam. You can register and sit it directly, without first earning the AI Practitioner, an Associate-level certification, or anything else. AWS removed hard prerequisites from its certification program years ago.

That said, "no required prerequisites" is not the same as "no preparation needed." AIP-C01 is a professional-level exam, and AWS publishes a clear set of recommended experience. Showing up without it is the most common reason people fail. Here's what you actually need to be ready.

What AWS Officially Recommends

According to the official AWS exam guide, the target candidate for AIP-C01 should have:

• 2+ years of experience building production-grade applications on AWS or with open-source technologies
• General AI/ML or data engineering experience
• At least 1 year of hands-on experience implementing generative AI solutions

In addition, AWS expects you to bring this AWS knowledge:

• Experience with AWS compute, storage, and networking services
• Understanding of AWS security best practices and identity management (IAM)
• Experience with AWS deployment and infrastructure-as-code (IaC) tools
• Familiarity with AWS monitoring and observability services
• Understanding of AWS cost-optimization principles

What's Explicitly Out of Scope

AWS also lists tasks the target candidate is not expected to perform — useful because it tells you what not to over-study:

• Model development and training (you're integrating foundation models, not building them)
• Advanced ML techniques (this isn't the ML Specialty exam)
• Data engineering and feature engineering (beyond what's needed to feed a RAG pipeline)

This is a developer exam, not a data-science or ML-research exam. Your focus should be on using foundation models well — selecting them, integrating them, securing them, and optimizing them — not on the math behind them.

Do You Need the AI Practitioner or ML Engineer First?

Neither is required, but they can help depending on where you're starting:

The AI Practitioner ($100, foundational) is a low-risk way to shore up responsible-AI and ML-fundamentals knowledge that also appears on the Professional exam. The Machine Learning Engineer – Associate is recommended by AWS as a natural predecessor but is not mandatory.

A Readiness Checklist

Before you book AIP-C01, you should be able to confidently say yes to most of these:

• I've called the Bedrock API (InvokeModel / Converse) from my own code
• I've built a working RAG pipeline using a vector store or Bedrock Knowledge Bases
• I've implemented at least one Bedrock Agent with Lambda-based tool use
• I understand IAM policies, VPC endpoints, and encryption for Bedrock
• I've configured Bedrock Guardrails for content filtering
• I can reason about model selection tradeoffs (cost, latency, context window)
• I've evaluated model output with automated metrics or human review

If you're checking most of these boxes, you have the practical foundation the exam assumes. If not, that's your study list.

Next Steps

Once you've confirmed you're ready, look at the AIP-C01 passing score and how scoring works, the exam cost and registration details, and the full AIP-C01 exam guide covering all five domains.

For the complete prep roadmap with a study timeline and resources, see our AWS Certified Generative AI Developer – Professional study guide.