AWS Certified Generative AI Developer – Professional (AIP-C01)

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Vendor: Amazon Web Services · Last verified: 3 October 2026 · How we rate

Fee $300Level AdvancedAccess Open to anyoneDifficulty 4/5

1. The quick verdict

Worth it if you build production generative AI applications on AWS, mainly with Amazon Bedrock: RAG, agents, guardrails and model integration. It is AWS’s most advanced and most expensive AI exam, and the only Professional-level one.

The one thing to know before you register: AWS recommends two years of building production apps plus a year of hands-on generative AI work, and the 750 pass mark is higher than other AWS AI exams.

2. Our ratings

CriterionScoreWhy
Difficulty (5 = hardest)●●●●○ 4/5Professional level, 75 questions in 3 hours, and a higher 750 pass mark.
Cost burden, including fee, prep and retake risk (5 = most expensive)●●●●○ 4/5$300 per attempt and the longest prep in AWS’s AI track.
Career value today (5 = highest)●●●●○ 4/5Generative AI skills on AWS are in demand, and this is the top AWS credential for them.
Skill transfer beyond this vendor (5 = most portable)●●●○○ 3/5RAG, agents, guardrails and evaluation transfer; Bedrock specifics do not.
Hands-on experience required (5 = must have built real things)●●●●● 5/5AWS recommends a year of hands-on generative AI on top of two years of production work.
Ease of access (5 = anyone can register)●●●●● 5/5Open to anyone.

3. At a glance

FieldDetails
LevelProfessional
AccessOpen to anyone
Exam feeUS$300
Retake policyFull fee each attempt, 14-day wait, no attempt limit
FormatPearson VUE, online proctored or test centre. 75 questions (65 scored, 10 unscored), 180 minutes. Multiple choice, multiple response, ordering and matching. English, Japanese, Korean, Simplified Chinese.
Passing score750 on a 100–1,000 scaled score
Coding neededReading code, API calls and config (Bedrock, Lambda, IAM). No writing code.
Formal prerequisitesNone
Realistic prerequisites2+ years building production applications on AWS or open-source stacks, general AI/ML or data engineering experience, and 1 year of hands-on generative AI work (AWS’s own recommendation).
Validity & renewal3 years. Passing it also renews AI Practitioner, ML Engineer – Associate and Data Engineer – Associate.
Estimated prep timeBuilding on Bedrock now: 40–60 hours. Experienced AWS developer new to generative AI: 80–100 hours.
Official exam guideAIP-C01 exam guide (AWS, PDF)

Prep-time estimates are our own judgment, not an AWS figure.

4. What the exam tests

DomainWeightWhat it means in plain language
Foundation Model Integration, Data Management, and Compliance31%Choosing and connecting foundation models, preparing data for RAG and vector stores, and meeting compliance needs.
Implementation and Integration26%Building the application: Bedrock APIs, agents, knowledge bases, and integration with other AWS services.
AI Safety, Security, and Governance20%Guardrails, prompt-injection defence, access control and responsible AI.
Operational Efficiency and Optimization12%Managing cost, latency and throughput in production.
Testing, Validation, and Troubleshooting11%Evaluating outputs, testing applications, and fixing failures.

Out of scope: model development and training, advanced ML techniques, and data or feature engineering.

5. Who should take it, and who shouldn’t

Take it if you are:

  • A developer or AI engineer shipping generative AI features on Amazon Bedrock.
  • A solution architect or tech lead responsible for RAG, agent or chatbot projects on AWS.
  • An ML engineer holding MLA who now works mostly with foundation models.

Skip it or wait if you are:

  • New to AWS or generative AI. Start with AI Practitioner and build real projects first; at $300 a failed attempt is expensive.
  • Training your own models. ML Engineer – Associate fits better.
  • Building on Claude through Anthropic’s API rather than Bedrock. CCDV-F or CCAR-F may fit better, if your employer is an Anthropic partner.

6. What to expect after passing

  • What it signals: You can design, build, secure and run production generative AI applications on AWS.
  • What it doesn’t prove: Model training skill, or generative AI work on other clouds and vendor APIs.
  • Where it’s asked for: Generative AI engineer and AI solution architect roles on AWS, and AWS partner requirements. Launched in 2026, so it is still building recognition, but it sits at the level employers value most.
  • Natural next step: None higher in AWS’s AI track. Add AWS Solutions Architect – Professional for breadth, or a governance credential if you lead programmes.

7. How to prepare

  • Official: The AIP-C01 exam guide, the AWS Skill Builder exam prep plan, and Amazon Bedrock documentation (models, Knowledge Bases, Agents, Guardrails, AgentCore, evaluations).
  • Practice: Use long scenario questions where the best answer balances quality, cost, latency and safety. A good set covers all four question types and explains each tradeoff.

Hands-on:

  1. Build a RAG app on a Bedrock Knowledge Base with a vector store, and measure answer quality (FM Integration, Testing).
  2. Add a Bedrock Agent that calls two tools, wrapped in Guardrails, then try to break it with prompt injection (Implementation, Safety).
  3. Compare two models on cost and latency for the same workload, and add caching or batching (Operational Efficiency).

8. Compare with

See all AWS guides → · Compare all AI certifications →

9. FAQ

How hard is the AWS Generative AI Developer – Professional exam?

Hard. It needs a 750 out of 1,000 across 75 questions in 3 hours, and assumes real production experience. Most candidates should hold AI Practitioner or MLA, or have built on Bedrock, before attempting it.

Is it about Claude?

Not specifically. Bedrock offers Claude alongside other models, and the exam tests the AWS platform, not any one model. For Claude-specific depth, Anthropic’s own exams go further.

Do I need MLA first?

No, there is no formal prerequisite. MLA helps if you come from classic ML; developers already building on Bedrock can go straight to AIP.

Does passing renew my other AWS certifications?

Yes. It renews AI Practitioner, ML Engineer – Associate and Data Engineer – Associate.

Sources

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