AIF-C01 and AIP-C01 are not competing certifications. AIF-C01 (AWS Certified AI Practitioner) is a foundational-level credential costing $100 that validates conceptual understanding of AI, machine learning, and generative AI on AWS. AIP-C01 (AWS Certified Generative AI Developer Professional) is a professional-level credential costing $300 that validates hands-on production implementation of generative AI applications using Amazon Bedrock, RAG, and AI agents. If you are new to AI on AWS, start with AIF-C01. If you are already building generative AI applications on AWS and want a credential that proves it, AIP-C01 is built for you. Most candidates should plan for both, in that order.
This guide breaks down exactly how these two certifications differ, who each is for, and how to plan your path between them.
AIF-C01 vs AIP-C01: Quick Comparison
| Factor | AIF-C01 (AI Practitioner) | AIP-C01 (GenAI Developer Professional) |
| Level | Foundational | Professional |
| Cost | $100 USD | $300 USD |
| Questions | 65 total (50 scored, 15 unscored) | 75 total (65 scored, 10 unscored) |
| Duration | 90 minutes | Up to 180 minutes |
| Passing score | 700 out of 1000 | 750 out of 1000 |
| Domains | 5 | 5 |
| Prerequisites | None | None formal, but 2+ years AWS and 1+ year GenAI experience recommended |
| Recommended background | Up to 6 months AI/ML exposure on AWS | 2+ years AWS, 1+ year hands-on generative AI development |
| Validity | 3 years | 3 years |
| Question types | Multiple choice, multiple response | Multiple choice, multiple response, ordering, matching |
| Status | Established, widely adopted | New, generally available after beta ended March 31, 2026 |
AIF-C01: What It Tests
AIF-C01 is designed for individuals who can demonstrate overall knowledge of AI, ML, generative AI technologies, and associated AWS services and tools, independent of a specific job role. It is intentionally broad and does not require coding, data science, or statistics background.
AIF-C01 Domains
| Domain | Weight | What It Covers |
| Domain 1: Fundamentals of AI and ML | 20% | Core AI and ML terminology, concepts, and the ML development lifecycle |
| Domain 2: Fundamentals of Generative AI | 24% | Generative AI concepts, capabilities, limitations, and use cases |
| Domain 3: Applications of Foundation Models | 28% | Selecting and using foundation models, prompt engineering basics, AWS services for building generative AI applications |
| Domain 4: Guidelines for Responsible AI | 14% | Responsible AI principles, bias, fairness, transparency |
| Domain 5: Security, Compliance, and Governance for AI Solutions | 14% | Security considerations, compliance frameworks, and governance for AI workloads on AWS |
Domain 3 carries the most weight at 28 percent. This domain covers AWS services for building generative AI applications, including foundation model selection and basic prompt engineering, conceptually rather than at implementation depth. This is the domain that most directly previews AIP-C01’s much deeper coverage of the same general territory.
Who AIF-C01 Is For
Analysts, cloud engineers, technical leads, business stakeholders working with AI initiatives, and anyone with up to 6 months of exposure to AI/ML technologies on AWS. AIF-C01 does not assume you build AI systems yourself. It assumes you need to understand AI/ML concepts, evaluate AI/ML solutions, and communicate about AI initiatives credibly. For complete preparation covering all five AIF-C01 domains, CertMage’s AIF-C01 exam dumps cover the current exam blueprint with detailed explanations.
AIP-C01: What It Tests
AIP-C01 is designed for individuals who perform a GenAI developer role, validating the ability to effectively integrate foundation models into applications and business workflows and implement generative AI solutions into production environments using AWS technologies.
AIP-C01 Domains
| Domain | Weight | What It Covers |
| Domain 1: Foundation Model Integration, Data Management, and Compliance | 31% | Selecting and configuring foundation models, designing architectures for dynamic model selection, data pipeline management, compliance standards |
| Domain 2: Implementation and Integration | 26% | Building with vector stores, Retrieval Augmented Generation (RAG), knowledge bases, Bedrock Agents |
| Domain 3: AI Safety, Security, and Governance | 20% | Responsible AI practices, security controls, governance frameworks for production AI systems |
| Domain 4: Operational Efficiency and Optimization | 12% | Cost optimization and performance tuning for generative AI workloads at scale |
| Domain 5: Testing, Validation, and Troubleshooting | 11% | Testing methodologies and troubleshooting for generative AI models and applications |
Domains 1 and 2 together represent 57 percent of AIP-C01. This is where the real gap with AIF-C01 becomes clear. AIF-C01’s Domain 3 (Applications of Foundation Models) introduces the concept of selecting foundation models and using AWS services to build generative AI applications. AIP-C01’s Domains 1 and 2 take this same general territory and go deep: configuring foundation models for production, designing resilient architectures with fallback mechanisms, implementing RAG with Bedrock Knowledge Bases, building vector stores, and designing Bedrock Agents.
Who AIP-C01 Is For
Developers who are already building generative AI applications on AWS in production, or close to it. AWS recommends 2 or more years of AWS experience and at least 1 year of hands-on generative AI development, plus familiarity with AWS compute, storage, networking, security (IAM, KMS, VPC), infrastructure as code, and observability through CloudWatch.
The Real Difference: Conceptual Understanding vs Production Implementation
The cleanest way to understand the gap between these two certifications is through a single example: foundation models.
AIF-C01 asks: Do you understand what a foundation model is, what generative AI can and cannot do, and which AWS services are generally used to build generative AI applications?
AIP-C01 asks: Can you select and configure a specific foundation model for a production use case, design a resilient architecture with fallback mechanisms if that model is unavailable, implement a RAG pipeline with Bedrock Knowledge Bases to ground that model’s responses in your organization’s data, secure that pipeline, optimize its cost at scale, and troubleshoot it when something goes wrong?
This is the difference between a certification that proves you can have an informed conversation about generative AI on AWS, and one that proves you can build, secure, and operate a generative AI system that an organization actually runs in production.
AIF-C01 vs AIP-C01: Cost and Time Investment
| Factor | AIF-C01 | AIP-C01 |
| Exam fee | $100 | $300 |
| Typical preparation time (with relevant background) | 2 to 4 weeks | 8 to 12 weeks |
| Typical preparation time (without relevant background) | 4 to 8 weeks | Not recommended without first completing AIF-C01-level foundation and gaining hands-on experience |
| Study materials cost | $20 to $50 (video course plus practice exams) | $50 to $150 (more advanced materials plus hands-on lab time) |
| Total realistic investment | $120 to $150 | $350 to $450 |
The $200 difference in exam fee reflects the difference in level. AIF-C01 at $100 is priced in line with other AWS Foundational certifications. AIP-C01 at $300 is priced in line with other AWS Professional certifications like Solutions Architect Professional and DevOps Engineer Professional, reflecting AWS’s assessment that production generative AI implementation skills command the same recognition as its other top-tier professional credentials.
Should You Take Both?
For most professionals working with AI on AWS, taking both certifications in sequence makes sense, but the order and timing depend on your current role.
| Your Situation | Recommended Path |
| New to AI/ML entirely, any AWS background | Start with AIF-C01. Build foundational vocabulary before anything else. |
| Solid AWS background, new to AI/ML | AIF-C01 first. The conceptual foundation is essential before AIP-C01’s implementation depth makes sense. |
| Already building generative AI applications on AWS in production | AIF-C01 first if you have not taken it (it is fast and inexpensive), then AIP-C01 to validate your production skills. |
| Data scientist or ML engineer with traditional ML background, new to generative AI | AIF-C01 to build generative AI-specific vocabulary, then AIP-C01 once you have hands-on generative AI implementation experience. |
| Already AIF-C01 certified, want to specialize in generative AI development | Go directly to AIP-C01 once you meet the recommended experience level (2+ years AWS, 1+ year hands-on GenAI). |
The honest reality for most candidates: do not skip from zero AI/ML knowledge directly to AIP-C01. AIP-C01’s domains assume you already understand foundation models, RAG, prompt engineering, and responsible AI at a conceptual level. AIF-C01 builds exactly this foundation in 2 to 4 weeks at a $100 cost. Skipping it to attempt AIP-C01 directly means learning AIF-C01-level concepts and AIP-C01-level implementation simultaneously, which significantly increases study time and risk of failure on a $300 exam.
AIF-C01 vs AIP-C01 vs MLA-C01: The Complete AWS AI/ML Picture in 2026
A third certification, AWS Certified Machine Learning Engineer – Associate (MLA-C01), sits alongside these two and is worth understanding to see the complete picture.
| Certification | Level | Cost | Focus |
| AIF-C01 | Foundational | $100 | Conceptual understanding of AI, ML, and generative AI on AWS |
| MLA-C01 | Associate | $150 | Building, tuning, deploying, and monitoring traditional ML pipelines using SageMaker |
| AIP-C01 | Professional | $300 | Production implementation of generative AI applications using Bedrock, RAG, and AI agents |
These three certifications represent three different specializations, not three steps of the same ladder. AIF-C01 is the foundation everyone should consider regardless of specialization. From there, MLA-C01 is for professionals building traditional ML pipelines (model training, feature engineering, SageMaker operations), while AIP-C01 is for professionals building applications on top of pre-trained foundation models (RAG, prompt engineering, AI agents). An organization’s AI strategy might require professionals with MLA-C01, AIP-C01, or both, depending on whether their work centers on custom model development or foundation model integration. For complete preparation covering the associate-level ML engineering path, CertMage’s MLA-C01 exam dumps cover the SageMaker-focused content this certification tests.
AIF-C01 vs AIP-C01: Salary and Career Impact
| Certification | Typical Roles | US Salary Range |
| AIF-C01 | AI-aware analysts, cloud engineers, technical leads, product managers working with AI initiatives | $90,000 to $135,000 |
| AIP-C01 | Generative AI developers, AI application engineers, GenAI solutions architects | $130,000 to $180,000 |
AIF-C01 functions less as a salary driver on its own and more as a credibility signal and foundation for further specialization. AIP-C01, as a new professional-level certification validating production generative AI skills, is positioned to command salary premiums similar to other AWS Professional certifications, particularly as organizations increasingly seek developers who can move generative AI projects from proof of concept to production.
FAQS
What is the difference between AIF-C01 and AIP-C01?
AIF-C01 (AWS Certified AI Practitioner) is a foundational certification costing $100 that validates conceptual understanding of AI, ML, and generative AI on AWS. AIP-C01 (AWS Certified Generative AI Developer Professional) is a professional certification costing $300 that validates hands-on ability to build, secure, and operate production generative AI applications using Amazon Bedrock, RAG, and AI agents.
Should I take AIF-C01 before AIP-C01?
Yes, for most candidates. AIF-C01 builds the foundational vocabulary and conceptual understanding (foundation models, generative AI, responsible AI, prompt engineering basics) that AIP-C01 builds upon at a much deeper, production implementation level. AIF-C01 is not a formal prerequisite for AIP-C01, but skipping it significantly increases the difficulty of AIP-C01 preparation for most candidates.
Is AIF-C01 a prerequisite for AIP-C01?
No. AIP-C01 has no formal prerequisites. However, AWS recommends 2 or more years of AWS experience and at least 1 year of hands-on generative AI development experience for AIP-C01, which in practice means most successful AIP-C01 candidates already have AIF-C01-level conceptual knowledge, whether from taking that exam or through equivalent experience.
How much harder is AIP-C01 than AIF-C01?
Significantly harder. AIF-C01 has 65 questions in 90 minutes with a 700/1000 passing score, testing conceptual knowledge. AIP-C01 has 75 questions in up to 180 minutes with a 750/1000 passing score, testing hands-on production implementation across more advanced topics like RAG architecture design, vector stores, and Bedrock Agents. Typical preparation time is 2 to 4 weeks for AIF-C01 versus 8 to 12 weeks for AIP-C01 for candidates with relevant backgrounds.
Can I take AIP-C01 without any AWS AI experience?
It is not recommended. AIP-C01’s domains assume familiarity with foundation models, generative AI concepts, and AWS AI services at the level AIF-C01 establishes, plus hands-on production experience. Candidates with zero AI/ML background attempting AIP-C01 directly face a much steeper learning curve across both foundational concepts and advanced implementation simultaneously.
What roles does AIF-C01 prepare you for versus AIP-C01?
AIF-C01 suits analysts, cloud engineers, technical leads, and business stakeholders who need to understand and evaluate AI initiatives but do not necessarily build AI systems themselves, with salaries typically $90,000 to $135,000. AIP-C01 suits generative AI developers and AI application engineers who build production systems, with salaries typically $130,000 to $180,000.
How does AIP-C01 compare to MLA-C01?
AIP-C01 (Professional, $300) and MLA-C01 (Associate, $150) represent different specializations rather than a ladder. MLA-C01 validates building and operating traditional ML pipelines using SageMaker. AIP-C01 validates building production applications on top of pre-trained foundation models using Bedrock, RAG, and AI agents. Organizations may need professionals with either or both depending on whether their AI strategy centers on custom model development or foundation model integration.
What is the passing score for AIF-C01 and AIP-C01?
AIF-C01 requires 700 out of 1000 on AWS’s scaled scoring system. AIP-C01 requires 750 out of 1000. Both use scaled scoring, meaning the raw number of correct answers needed varies between exam forms based on question difficulty.
How long are AIF-C01 and AIP-C01 valid?
Both certifications are valid for 3 years from the date you pass, consistent with other AWS certifications.
Is AIF-C01 worth it if I am going to take AIP-C01 anyway?
Yes, for most candidates. At $100 and 2 to 4 weeks of preparation, AIF-C01 is a low-cost, low-time-investment way to build the conceptual foundation that makes AIP-C01 preparation more manageable. Even candidates confident in their generative AI knowledge often find AIF-C01 reinforces AWS-specific terminology and service knowledge that directly supports AIP-C01 preparation.
