The AWS Certified AI Practitioner (AIF-C01) is a foundational-level credential that validates working knowledge of artificial intelligence, machine learning, and generative AI concepts on AWS. The exam costs $100, lasts 90 minutes, spans 65 questions across five domains, and requires a scaled passing score of 700 out of 1,000. It is designed for professionals who use AI and ML technologies but do not necessarily build them — business analysts, product managers, sales professionals, line-of-business managers, and IT support staff who need to demonstrate practical AI fluency without writing code or training models. AWS reports that employers are willing to pay 47% more for AI-skilled IT professionals, making the AIF-C01 one of the most cost-effective entry points into AI certification. The credential is valid for three years and positions you for associate-level AWS certifications in data engineering, machine learning, and solutions architecture. For a broader view of which AWS credentials deliver the strongest career return, see our AWS certifications guide for 2026.
What Is the AIF-C01 Exam
The AIF-C01 launched as AWS’s foundational AI certification, distinct from the Cloud Practitioner (CLF-C02) because it focuses entirely on AI, ML, and generative AI rather than general cloud concepts. The Cloud Practitioner exam contains only one task statement related to AI, while the entire AIF-C01 exam content outline centers on artificial intelligence, machine learning, and generative AI technologies. The official exam guide states that the exam validates a candidate’s ability to understand AI, ML, and generative AI concepts, determine the correct types of AI technologies for specific use cases, and use those technologies responsibly in business contexts.
The target candidate should have up to six months of exposure to AI and ML technologies on AWS and uses, but does not necessarily build, AI and ML solutions on the platform. This scoping is deliberate and tells you exactly what not to study. The exam guide explicitly lists tasks that are out of scope: developing or coding AI or ML models, performing hyperparameter tuning, building and deploying AI or ML pipelines, and conducting mathematical or statistical analysis of models. Instead, the AIF-C01 tests conceptual fluency — identifying the right AWS service for a given use case, understanding the foundation model lifecycle from data selection through deployment, recognizing prompt engineering techniques, and applying responsible AI guidelines.
The exam recommends familiarity with core AWS services such as Amazon EC2, Amazon S3, AWS Lambda, and Amazon SageMaker, along with the shared responsibility model for security and compliance, IAM fundamentals, global infrastructure concepts including Regions and Availability Zones, and service pricing models. For candidates who already hold the Cloud Practitioner or an associate-level certification, AWS confirms that you can skip the foundational cloud courses and start directly with the free AI training included in the exam prep plans.
Exam Format and Scoring
The AIF-C01 exam consists of 65 questions — 50 scored and 15 unscored — completed within 90 minutes. The unscored items are pilot questions that AWS uses to evaluate future exam content; you will not know which ones count toward your result. The exam costs $100 USD and is delivered through Pearson VUE testing centers or online proctoring. It is offered in twelve languages including English, Japanese, Korean, Portuguese, Simplified Chinese, and Spanish, making it accessible to candidates worldwide.
| Detail | Specification |
|---|---|
| Exam code | AIF-C01 |
| Level | Foundational |
| Questions | 65 (50 scored, 15 unscored) |
| Duration | 90 minutes |
| Cost | $100 USD |
| Passing score | 700 (scaled 100–1,000) |
| Validity | 3 years |
| Question types | Multiple choice, multiple response, ordering, matching, case study |
Question formats extend well beyond standard multiple choice. The exam includes multiple-response items that require selecting all correct answers from five or more options, ordering tasks that ask you to arrange three to five steps in the correct sequence, matching questions that pair items from two lists, and case-study scenarios where multiple questions reference the same situation. AWS uses a scaled scoring model from 100 to 1,000, and the minimum passing score for foundational-level exams is 700. The exam employs a compensatory scoring model, meaning your overall score determines pass or fail — you do not need to achieve a passing score in each individual domain section. AWS establishes the passing standard through a criterion-referenced process called the modified Angoff technique, where a panel of content experts estimates the difficulty of each question relative to a minimally qualified candidate.
The Five Weighted Domains
The AIF-C01 blueprint distributes its scored questions across five content domains whose weightings directly inform how you should allocate study time. Two domains — Fundamentals of Generative AI and Applications of Foundation Models — together account for 52% of the scored content. If your preparation time is limited, concentrate on Domains 2 and 3 first because they carry the highest combined weight.
| Domain | Title | Weight |
|---|---|---|
| 1 | Fundamentals of AI and ML | 20% |
| 2 | Fundamentals of Generative AI | 24% |
| 3 | Applications of Foundation Models | 28% |
| 4 | Guidelines for Responsible AI | 14% |
| 5 | Security, Compliance, and Governance | 14% |
Domain 1 covers AI and ML fundamentals at 20% of scored content. It tests your understanding of basic AI terminology, the differences between AI, ML, and deep learning, types of inferencing including batch and real-time, and the three learning paradigms: supervised, unsupervised, and reinforcement learning. You also need to describe the ML development lifecycle — from data collection through model training, evaluation, deployment, and monitoring — and identify relevant AWS services for each pipeline stage. Performance metrics including accuracy, the Area Under the ROC Curve (AUC), and the F1 score appear alongside business metrics like cost per user and return on investment.
Domain 2 explores generative AI concepts at 24% weight. You must understand foundational concepts such as tokens, chunking, embeddings, vectors, prompt engineering, transformer-based large language models, foundation models, multi-modal models, and diffusion models. This domain also covers the foundation model lifecycle from data selection through feedback, the advantages of generative AI including adaptability and responsiveness, and its disadvantages such as hallucinations, interpretability limitations, inaccuracy, and nondeterminism. AWS infrastructure for generative AI is tested here, including Amazon SageMaker JumpStart, Amazon Bedrock, PartyRock, and Amazon Q.
Domain 3, the heaviest section at 28%, tests practical foundation model skills. You need to understand model selection criteria that weigh cost, modality, latency, context-window size, and customization options. Inference parameters such as temperature and input or output length are tested, as are retrieval-augmented generation using Amazon Bedrock Knowledge Bases, vector databases, prompt engineering techniques, prompt attack risks, and the cost tradeoffs among different customization methods. Training, fine-tuning, and data preparation processes appear here, along with evaluation metrics such as ROUGE, BLEU, and BERTScore.
Domains 4 and 5 address responsible AI and security governance at 14% each. Domain 4 covers guidelines for responsible AI, including bias detection, fairness, transparency, and the ability to explain model decisions. Domain 5 tests security, compliance, and governance for AI solutions, including AWS security controls for AI workloads, data privacy, encryption requirements, and compliance frameworks that apply to generative AI applications. Together these domains account for 28% of the exam, so skipping responsible AI and security preparation is a costly mistake.
Key AWS AI Services
The AIF-C01 does not test every AWS service. The exam guide defines an explicit list of in-scope services, and your preparation should center on those. Amazon Bedrock is the most critical service to master. AWS describes it as a platform that gives you access to hundreds of foundation models from leading AI companies, along with evaluation tools to pick the best model based on your performance and cost needs. Bedrock Knowledge Bases enable retrieval-augmented generation by connecting models to your private data sources, Bedrock Guardrails apply safety filters to block harmful content, and fine-tuning capabilities let you adapt foundation models to specific domains.
Beyond Bedrock, study the Amazon SageMaker ecosystem in depth. SageMaker JumpStart provides access to pre-trained foundation models for quick deployment, SageMaker Data Wrangler handles data preparation, and SageMaker Feature Store and Model Monitor support the broader ML lifecycle. Amazon Q delivers AI-powered business assistance. The managed AI services each serve distinct, testable functions: Amazon Transcribe converts speech to text, Amazon Polly generates speech from text input, Amazon Comprehend performs natural language processing and sentiment analysis, Amazon Lex builds conversational interfaces, Amazon Rekognition handles computer vision and image analysis, and Amazon Textract extracts structured data from documents and forms. The exam guide also references PartyRock, an Amazon Bedrock Playground designed for hands-on experimentation with foundation models without writing code. For deeper hands-on practice with AWS application development patterns, our Developer Associate DVA-C02 study guide covers the services that build on these AI foundations.
Study Plan and Timeline
AWS recommends following a structured four-step exam prep plan available on AWS Skill Builder. Step one is reviewing the exam guide thoroughly. Step two involves refreshing your knowledge with digital courses, AWS Builder Labs, and AWS Cloud Quest. Step three focuses on reviewing exam scope and practicing with exam-style questions and flashcards. Step four is taking the AWS Certification Official Practice Exam to assess readiness. A focused candidate with some cloud familiarity can prepare in four to six weeks, and most of that time should go to Domains 2 and 3, which together carry 52% of the exam weight.
Here is a practical five-week study plan organized by domain weight:
- Week 1 — Domain 1 (20%): Study AI and ML fundamentals. Learn the ML lifecycle end to end, supervised versus unsupervised versus reinforcement learning, and model performance metrics including accuracy, AUC, and F1 score.
- Week 2 — Domain 2 (24%): Focus on generative AI. Master tokens, embeddings, vectors, transformer architecture, foundation models, diffusion models, and their advantages and disadvantages.
- Week 3 — Domain 3 (28%): Dedicate the most time here. Practice model selection criteria, RAG with Bedrock Knowledge Bases, prompt engineering patterns, the cost gradient among customization methods, and evaluation metrics like ROUGE and BLEU.
- Week 4 — Domains 4 and 5 (14% each): Cover responsible AI and security. Study bias and fairness, transparency, AWS AI governance tools, data privacy, encryption, and compliance standards.
- Week 5 — Practice and review: Take full-length timed practice exams. Aim for 800 or higher consistently before scheduling the real exam, and revisit your weakest domains.
If you do not pass on the first attempt, AWS requires a 14-day waiting period before retaking the exam. There is no limit on total attempts, but you must pay the full registration fee for each retake. AWS also provides a 50% discount voucher when you recertify or upgrade a certification, available in the benefits section of your AWS Certification account. The AIF-C01 is valid for three years, and you can recertify by passing the latest version of this exam or by earning the AWS Certified Machine Learning Engineer — Associate, which automatically recertifies the AI Practitioner credential.
Sources
- AWS Certified AI Practitioner (AIF-C01) Exam Guide — AWS Training and Certification
- AWS Certified AI Practitioner — Official Page — Amazon Web Services
- AWS Certification FAQs — Amazon Web Services
- AWS Certification After Testing Policies — Amazon Web Services
- Amazon Bedrock — Official Page — Amazon Web Services