AWS Generative AI Developer Professional (AIP-C01)
Prove your expertise building production generative AI applications on AWS
Exam Quick Facts
Exam Code
AIP-C01
Questions
75
Duration
180 minutes
Passing Score
750/1000 (~75%)
Cost
$300 USD
Valid For
3 years
Last Updated
2025
Difficulty
Expert
AWS Certified Generative AI Developer – Professional Cheat Sheet (AIP-C01)
Every Bedrock feature, RAG and agent pattern, and safety / observability decision the AIP-C01 exam actually tests — organised by the five exam domains with reference architectures and runbooks
Certification Overview
The AWS Certified Generative AI Developer Professional (AIP-C01) is the newest and most advanced AI certification from AWS. It validates your ability to design, implement, and deploy generative AI solutions using AWS services — primarily Amazon Bedrock and SageMaker. This is not a conceptual exam; it expects hands-on experience building AI-powered applications.
The AIP-C01 covers the full lifecycle of generative AI development: selecting appropriate foundation models, integrating them into applications, implementing retrieval-augmented generation (RAG), fine-tuning models, ensuring AI safety and governance, and optimizing inference performance. It's the natural progression from the AI Practitioner for developers who build with generative AI.
With generative AI transforming every industry, this certification positions you at the cutting edge of cloud-based AI development. It demonstrates that you can move beyond prompting a chatbot to actually architecting production-grade AI systems with proper guardrails, monitoring, and cost optimization.
Who Should Take This Exam?
This certification is built for software developers, ML engineers, and AI application developers who are actively building generative AI solutions on AWS. If you work with Amazon Bedrock, fine-tune foundation models, implement RAG pipelines, or deploy AI-powered features in production applications, this cert validates that expertise.
It's also relevant for senior developers and tech leads responsible for AI architecture decisions — choosing between foundation models, designing prompt chains, implementing content filtering, and ensuring responsible AI practices. You should have at least two years of development experience and significant hands-on time with AWS AI services before attempting this exam.
Exam Domains
FM Integration and Data Preparation
The largest domain, covering foundation model selection via Amazon Bedrock, data preparation for fine-tuning and RAG, embedding generation and vector stores, prompt engineering patterns, and compliance considerations when working with training data.
Implementation and Integration
Tests your ability to integrate foundation models into applications using Bedrock APIs, implement RAG with knowledge bases, design multi-step agent workflows with Bedrock Agents, handle streaming responses, and manage model invocation patterns.
AI Safety, Security, and Governance
Covers implementing guardrails for AI outputs, content filtering, PII detection and redaction, model access controls via IAM, audit logging, responsible AI frameworks, and how to handle harmful or biased model outputs in production.
Operational Efficiency
Tests knowledge of optimizing inference costs, managing model endpoints, implementing caching strategies, monitoring model performance with CloudWatch, A/B testing model versions, and scaling AI workloads efficiently.
Testing and Validation
Covers evaluation methods for generative AI outputs, automated testing pipelines for AI applications, human-in-the-loop evaluation, benchmarking model quality, and continuous validation strategies for production AI systems.
Ready to test your AIP-C01 knowledge?
Practice with our free Generative AI Developer Professional (AIP-C01) exam — 75 questions with detailed explanations. No signup required.
Start Free Practice ExamStudy Strategy
This is a hands-on professional-level exam, so start by building real generative AI applications on AWS. If you haven't already, create a RAG pipeline using Bedrock Knowledge Bases with an S3 data source, implement a Bedrock Agent with tools, and fine-tune a model using Bedrock custom model training. These practical experiences are essential.
Focus heavily on FM Integration and Data Preparation (31%) — the largest domain. You need to understand which Bedrock foundation models are suited for which tasks, how to prepare data for fine-tuning, how embedding models work for RAG, and prompt engineering best practices. Also master Implementation and Integration (26%), especially Bedrock APIs, agent orchestration, and streaming patterns.
Budget 6-10 weeks of study, assuming you already have development experience with Bedrock. Review AWS documentation for Bedrock, SageMaker inference, and Guardrails for Amazon Bedrock thoroughly. This exam includes 10 unscored questions mixed in with the 65 scored ones, so don't be surprised by unexpected topics.
Key AWS Services to Know
- Amazon Bedrock — foundation model access, Knowledge Bases, Agents, Guardrails
- Amazon SageMaker — model training, fine-tuning, inference endpoints
- Amazon Bedrock Guardrails — content filtering and safety controls
- Amazon OpenSearch Serverless — vector search for RAG
- Amazon Kendra — intelligent search with generative AI
- AWS Lambda — serverless integration for AI pipelines
- Amazon S3 — data storage for training data and knowledge bases
- AWS Step Functions — orchestrating multi-step AI workflows
- Amazon CloudWatch — monitoring AI application performance
- AWS IAM — access control for model invocation and data
- Amazon DynamoDB — conversation state and session management
- Amazon API Gateway — exposing AI endpoints to applications
Career Impact
Generative AI developers are among the most sought-after professionals in tech today. This certification validates expertise that commands premium compensation — AI engineer and ML engineer roles with generative AI focus typically pay $160K-$220K+, with senior positions exceeding $250K at major tech companies.
Beyond salary, this certification positions you for emerging roles like AI Application Architect, GenAI Platform Engineer, and AI Product Developer. As organizations race to integrate generative AI into their products and workflows, certified professionals who can build production-grade AI systems with proper governance are in exceptionally high demand.
Frequently Asked Questions
Should I get the AI Practitioner before the Generative AI Developer Professional?
It's not required but recommended if you're new to AWS AI services. The AI Practitioner covers conceptual foundations, while this exam expects hands-on implementation experience. If you already work with Bedrock daily, you can skip to this exam.
How hands-on is this exam compared to other AWS professional certs?
Very hands-on. Expect questions about specific Bedrock API patterns, RAG implementation details, guardrails configuration, and model fine-tuning workflows. This is not a conceptual exam — you need real experience building with these services.
What foundation models should I know for this exam?
Focus on models available through Amazon Bedrock: Anthropic Claude, Amazon Titan, Meta Llama, Stability AI, and Cohere. Understand the strengths and use cases for each, and how to select the right model for specific tasks.
Is this exam harder than the Solutions Architect Professional?
They target different expertise. The SAP covers broad architecture across all AWS services, while the AIP-C01 goes deep into generative AI. If you're an AI developer, this may feel more natural but requires very specific domain knowledge.
Ready to test your AIP-C01 knowledge?
Practice with our free Generative AI Developer Professional (AIP-C01) exam — 75 questions with detailed explanations. No signup required.
Start Free Practice Exam