Aws Generative Ai Developer Certification: Aip-c01 Exam Guide For 2026

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The AWS Generative AI Developer Certification, officially AWS Certified Generative AI Developer – Professional (AIP-C01), validates advanced skills in integrating foundation models into production applications and business workflows on AWS. It covers RAG, vector stores, prompt engineering, agentic AI, security, governance, optimization, evaluation, monitoring, and troubleshooting. The exam has 75 questions, lasts 180 minutes, and uses a 750 passing score. It is aimed at experienced developers with cloud, AI/ML, data engineering, and hands-on generative AI implementation experience in production environments.
What Is the AWS Generative AI Developer Certification?
The AWS Generative AI Developer Certification is a Professional-level credential for developers who build, integrate, deploy, secure, and operate generative AI applications on AWS.
Its official name is AWS Certified Generative AI Developer – Professional, and the exam code is AIP-C01.
This is not a basic AI-awareness exam. The certification focuses on taking generative AI beyond proof of concept and into production. Candidates need to understand ...
... how foundation models connect with enterprise data, APIs, vector stores, retrieval systems, security controls, evaluation, and application workflows.
An AWS Generative AI Developer should be able to make practical engineering decisions: when to use RAG, how to chunk documents, how an agent should call tools safely, and how to balance model quality, latency, security, and efficiency.
Who Should Take the AWS Certified Generative AI Developer Professional Exam?
The AWS Certified Generative AI Developer Professional certification is aimed at experienced developers and technical professionals working with cloud applications and GenAI.
AWS describes the target candidate as someone with:
Two or more years of experience building production-grade applications on AWS or open-source technologies

General AI/ML or data engineering experience

About one year of hands-on experience implementing generative AI solutions

Familiarity with AWS compute, storage, networking, security, deployment, monitoring, and cost optimization

No specific AWS certification is required first. Experience with foundational or associate-level AWS knowledge can still make preparation easier.
AIP-C01 Exam Format
The AIP-C01 exam contains 75 questions and provides 180 minutes of testing time.
Exam Detail
AIP-C01
Certification level
Professional
Exam code
AIP-C01
Total questions
75
Scored questions
65
Unscored questions
10
Exam time
180 minutes
Passing score
750 on a 100–1,000 scale
Question types
Multiple choice and multiple response
Testing options
Test center or online proctored

The ten unscored questions are not identified during the exam. Unanswered questions are scored as incorrect, and there is no penalty for guessing.
AWS Certified Generative AI Developer – Professional Exam Domains
Domain
Weight
Foundation Model Integration, Data Management, and Compliance
31%
Implementation and Integration
26%
AI Safety, Security, and Governance
20%
Operational Efficiency and Optimization for GenAI Applications
12%
Testing, Validation, and Troubleshooting
11%

1. Foundation Model Integration, Data Management, and Compliance — 31%
This is the largest domain. Study foundation model selection, data preparation, vector stores, embeddings, retrieval, Retrieval Augmented Generation (RAG), prompt engineering, prompt governance, chunking, metadata, hybrid search, and reranking.
Candidates should understand why one retrieval or model design fits a requirement better than another.
2. Implementation and Integration — 26%
This domain covers how GenAI components become working applications.
Key areas include agentic AI systems, tool integration, model deployment, enterprise application integration, foundation model APIs, streaming responses, event-driven architectures, CI/CD, model routing, and fallback patterns.
The exam may test how agents manage state, call tools, interact with enterprise systems, and recover safely when a model or service fails.
3. AI Safety, Security, and Governance — 20%
Candidates should understand prompt injection, jailbreak risks, PII protection, IAM, least-privilege access, network isolation, guardrails, audit logging, data lineage, compliance monitoring, responsible AI, and hallucination reduction.
The better answer in a scenario is often a layered security design rather than relying on one control.
4. Operational Efficiency and Optimization — 12%
The AWS Gen AI Developer Professional exam also tests production operations.
Important topics include token usage, context-window optimization, caching, throughput, model selection, latency, response streaming, monitoring, and capacity planning.
A useful decision framework is to balance quality, latency, security, and operational efficiency rather than automatically choosing the most capable model.
5. Testing, Validation, and Troubleshooting — 11%
Candidates should know how to evaluate relevance, factual accuracy, consistency, retrieval quality, agent performance, hallucination rates, semantic drift, latency, and business outcomes.
Troubleshooting can include prompt failures, retrieval problems, embedding issues, context-window limits, API integration errors, and inconsistent model responses.
Skills That Matter Most for an AWS Generative AI Developer
RAG and Vector Search
Understand the full RAG flow: document ingestion, preprocessing, chunking, embedding generation, vector storage, retrieval, reranking, prompt augmentation, and response generation.
Candidates should also understand when semantic search, keyword search, or hybrid retrieval is appropriate.
Foundation Model Selection
Foundation models differ in capabilities, latency, context limits, modality, and operational characteristics.
An AWS Certified Generative AI Developer should be able to select models according to business and technical requirements instead of automatically choosing the largest model available.
Agentic AI
The current AWS Gen AI Professional Certification scope includes agentic AI.
Study:
Agents and tools

Memory and state

Multi-step workflows

Tool permissions

Human approval

Stopping conditions

Agent failure handling

Multi-agent coordination

This is increasingly important because production AI applications may need to complete tasks, access external systems, and make controlled decisions rather than only generate text.
Security, Evaluation, and Observability
Security should not be treated as a final step.
Consider data access, prompts, retrieval permissions, tool execution, output filtering, privacy, logging, and auditing as parts of one production system.
Candidates should also understand evaluation datasets, tracing, user feedback, model evaluations, and operational metrics.
How to Prepare for the AWS Gen AI Developer Certification
Use the exam-domain percentages to structure your study plan.
Start with the AIP-C01 blueprint. Give the most study time to the 31%, 26%, and 20% domains.

Build a RAG application. Ingest documents, generate embeddings, configure retrieval, and evaluate answer quality.

Build an agent workflow. Give the agent controlled access to tools and test permissions, failures, and approval processes.

Practice security scenarios. Review IAM, PII protection, guardrails, prompt-injection defenses, and logging.

Measure production behavior. Track latency, token use, retrieval quality, response quality, and failure patterns.

Troubleshoot deliberately. Break prompts, retrieval, permissions, and API integrations and then diagnose the cause.

Use AIP-C01 practice questions. Study why incorrect options fail instead of simply memorizing the correct answers.

Hands-on work matters because AIP-C01 focuses heavily on production decisions rather than isolated definitions.
AWS GenAI Certification vs AWS Certified AI Practitioner
The AWS Generative AI Certification path supports professionals at different experience levels.
AWS Certified AI Practitioner is a foundational certification focused on AI, machine learning, generative AI concepts, and practical business applications.
The AWS Certified Generative AI Developer – Professional is significantly more technical and implementation-focused.
It expects candidates to understand:
RAG architectures

Foundation model integration

Vector databases

Agentic AI

Prompt engineering

Security and governance

Application integration

Monitoring

Evaluation

Troubleshooting

Performance optimization

If you mainly need AI literacy, a foundational certification may be a better starting point.
If you already develop cloud applications and want to validate production-level GenAI engineering skills, the AWS Generative AI Developer Professional certification is the more relevant target.
What Makes the AIP-C01 Exam Challenging?
The difficult part is not simply remembering what Amazon Bedrock or another AWS service does.
The challenge is choosing the best architecture when several answers may be technically possible.
For example, one scenario might combine retrieval accuracy, IAM permissions, latency, resilience, token usage, and governance requirements.
The strongest answer usually satisfies the stated requirement without introducing unnecessary complexity or operational risk.
When working through scenario questions, ask:
What requirement is mandatory?

Which failure mode matters most?

Where should security controls be applied?

How will output quality be measured?

Does the application really require an agent?

Would RAG solve the requirement more effectively than model customization?

What is the simplest production-ready design?

This approach is more useful than memorizing a long list of AWS services.
Build a Production-Ready AIP-C01 Study Plan
Treat the AWS Generative AI Developer Certification as a production engineering certification rather than an AI terminology exam.
Prioritize RAG, vector databases, foundation model integration, prompt engineering, agentic AI, Amazon Bedrock, security, governance, observability, model evaluation, troubleshooting, latency, and optimization.
Build small working applications and connect every technical decision back to one of the five AIP-C01 exam domains.
If you can explain not only how a GenAI application works but also how to secure it, evaluate it, monitor it, troubleshoot it, scale it, and improve it, you are preparing at the level expected from an AWS Certified Generative AI Developer – Professional.