AWS Certified Generative AI Developer - Professional (AIP-C01): The Complete Exam Guide

What AIP-C01 exam This Exam Actually Certifies

The AWS Certified Generative AI Developer - Professional exam validates something narrower and more practical than "knows AI." It checks whether you can take a foundation model and turn it into something that survives contact with production: wiring it into an application, grounding it against data through retrieval, keeping it safe and compliant, watching what it costs, and fixing it when it breaks. AWS's own framing is that it tests a candidate's ability to integrate foundation models into applications and business workflows, not to build or train models from scratch. Model development, feature engineering, and advanced ML research techniques are explicitly listed as out of scope for the target candidate.

That distinction matters for anyone deciding whether this is the right exam. If your job is closer to "I fine-tune and train models" or "I do data science," this exam is not built to measure that. If your job is closer to "I build the RAG pipeline, the agent, the prompt layer, and the guardrails that sit around a model AWS already hosts," this is squarely it.

Who This AIP-C01 exam Exam Is For (and Who It's Not For)

AWS's target candidate has 2 or more years building production applications on AWS or with open source technologies, general AI/ML or data engineering exposure, and at least 1 year of hands-on experience actually implementing generative AI solutions. The exam guide also expects familiarity with AWS compute, storage, networking, security and identity practices, deployment and infrastructure-as-code tooling, monitoring, and cost optimization principles going in. This is not written for someone learning what an API Gateway does while also learning what a vector store does.

It's a good fit for application developers who've shipped a Bedrock-backed feature, solutions architects designing GenAI systems, and MLOps or platform engineers who own the infrastructure around foundation models. It's a poor fit for someone with no hands-on AWS build experience, someone hoping to learn generative AI concepts for the first time from the exam itself, or someone whose actual work is model training rather than model integration.

AWS does not require any specific certification before you sit this exam, but it suggests candidates would benefit from having earned the AWS Certified AI Practitioner, AWS Certified Solutions Architect - Associate, AWS Certified Machine Learning Engineer - Associate, or AWS Certified Data Engineer - Associate first. None of those are enforced by the registration system; they're a recommendation, not a gate.

AIP-C01 exam Exam Format and Structure

You get 180 minutes for 75 questions, a little under 2.5 minutes per question on average, though the questions themselves are scenario-heavy, so that budget goes fast. Of the 75, 65 count toward your score and 10 are unscored items AWS is quietly testing for future versions of the exam. You won't know which 10 those are, so there's no shortcut of trying to spot and skip them.

Questions come in two formats. Multiple choice gives you one correct answer among four options. Multiple response requires you to select every correct answer out of five or more options, with partial credit not given, meaning a near-miss on a multi-select question counts the same as a complete miss. Expect long, scenario-style stems: a description of a company's architecture, constraints, and goals, followed by a question about which service or pattern satisfies them. There's no penalty for guessing, and unanswered questions are marked wrong, so leaving something blank never helps you.

Domains Covered

AWS structures the exam around five content domains with fixed weightings. This determines how much of your preparation time should go where far more reliably than any third-party study guide.

Domain 1, by far the largest slice, breaks down into six tasks worth knowing in detail: designing GenAI solutions against business requirements, selecting and configuring foundation models (including resilience patterns like Bedrock Cross-Region Inference and fine-tuning approaches like LoRA through SageMaker AI), building data validation and processing pipelines for multimodal input, designing vector store architectures (Bedrock Knowledge Bases, OpenSearch Service with its Neural plugin, Aurora with pgvector, DynamoDB for metadata), designing retrieval mechanisms including chunking strategies and embedding selection, and implementing prompt engineering and governance through tools like Bedrock Prompt Management and Prompt Flows.

Domain 3 is worth taking seriously even though it's not the largest domain, because it's tested in depth: content safety filtering through Bedrock Guardrails, PII detection with Comprehend and Macie, data lineage and audit logging through Glue Data Catalog and CloudTrail, and responsible AI practices like bias evaluation and model cards. If your GenAI experience so far has been mostly "get the model to answer well" without touching guardrails, PII handling, or governance logging, this domain deserves dedicated attention.

Domains 2, 4, and 5 track the exam's broader stated goals of integrating FMs into applications and business workflows, implementing agentic AI solutions, optimizing for cost and performance, and troubleshooting and monitoring GenAI applications in production. Together they cover roughly half the scored content, so treating them as an afterthought after Domain 1 is a common and costly mistake.

Cost Breakdown

The base exam fee is 300 USD, consistent with AWS's standard pricing for Professional-level certifications. The listed price is in US dollars, and AWS notes the actual charge can vary by country due to foreign exchange rates, so what you're billed may differ slightly depending on where you register.

There's no separate "retake fee" tier. If you fail, you pay the full 300 USD again for each subsequent attempt. AWS requires a 14-calendar-day wait between a failed attempt and your next one, and there's no cap on how many times you can retake it. Once you pass, you cannot sit the same exam again for two years unless AWS releases an updated version with a new guide and exam code.

Prerequisites and Eligibility

There are no enforced prerequisites. AWS's certification system will let anyone register and schedule the exam regardless of prior certifications. What functions as a barrier is the target candidate profile: 2 or more years of production application experience and 1 year of hands-on generative AI implementation work. Walking in without that background, even if you're technically allowed to register, is a very different exam than walking in with it.

How the AIP-C01 exam Exam Is Scored and What Passing Requires

Scores are reported on a scaled range of 100 to 1,000, with 750 as the minimum to pass. AWS uses this scaled model to equate difficulty across different exam forms, so 750 doesn't map cleanly to a fixed raw percentage. The exam uses compensatory scoring, meaning you don't need to individually clear each domain; a weak Domain 5 score can be offset by strength elsewhere, as long as your overall scaled score clears 750. Your score report includes a breakdown by section for diagnostic purposes, but AWS itself cautions against reading too much into section-level detail, since domain weightings aren't equal and a handful of questions in a smaller domain can swing that section's reported performance disproportionately.

Renewal and Recertification

The certification is valid for three years from the date you earn it. To keep it active, AWS requires you to take the current version of the corresponding exam before expiration; unlike some Foundational-level AWS certifications, there's no alternate path here like a game-based recertification track. Continuing education credits are not accepted as a substitute. If AIP-C01 gets replaced by a newer exam code before your three years are up, recertifying means passing whatever the current version is at that time, not the original AIP-C01.

How This AIP-C01 exam Exam Compares to Its Closest Alternatives

There isn't a direct one-to-one competitor, since this specific combination of AWS-native, GenAI-integration-focused, and Professional-level is fairly unusual as of when this page was written. The nearest comparisons worth knowing about:

Within AWS's own catalog, the AWS Certified Machine Learning Engineer - Associate sits at a lower level and covers a broader ML lifecycle rather than specializing in generative AI integration, while the AWS Certified AI Practitioner is Foundational-level and aimed at a non-technical or early-career audience rather than hands-on builders.

Outside AWS, the closest comparisons are Google Cloud's Professional Machine Learning Engineer certification, which is broader than generative AI specifically and covers the full ML engineering lifecycle rather than FM integration alone, and Microsoft's Azure AI Engineer Associate, which centers on Azure AI services rather than being generative-AI-specific in the way AIP-C01 is. None of these map cleanly onto AIP-C01's particular focus on RAG, vector stores, and production FM integration, so treat any "equivalent to X" claim you see elsewhere with some skepticism.

Preparation Timeline

This varies a lot based on how much hands-on Bedrock and RAG-building experience you already have going in, so take the following as general guidance rather than a fixed rule. A candidate who already matches AWS's target profile, meaning production GenAI work and solid general AWS experience, is often looking at 4 to 6 weeks of focused review: working through the exam guide domain by domain, building or reviewing a small RAG pipeline hands-on, and spending time in Domain 3's safety and governance material since that's where conceptual gaps tend to hide even for otherwise strong builders. Someone with strong AWS experience but comparatively thin generative-AI-specific implementation time should expect closer to 2 to 3 months, with extra time going toward actually building something with Bedrock Knowledge Bases, Guardrails, and Prompt Management rather than just reading about them, since the exam's scenario-style questions reward having touched these services directly.