Training course
This is a free, self-paced reading course for AIF-C01: AWS Certified AI Practitioner — the foundational credential for people who make decisions about AI, machine learning, and generative AI on AWS without necessarily building the models themselves. You will not write code or tune hyperparameters for this exam, and the course does not teach you to. It teaches the vocabulary and the judgment the exam actually measures: when generative AI fits a business problem and when it does not, whether prompt engineering, RAG, or fine-tuning is the right answer, which managed AWS service solves which task, and how bias, transparency, security, and governance obligations constrain what you can ship.
The course is organised as one module per official exam domain, in the order AWS publishes them, and each module carries the domain’s published weight. That matters more here than on most foundational exams, because AIF-C01 is not evenly spread: generative AI and the applications of foundation models together account for more than half the exam, so they account for more than half the course. Every module ends with its key terms and suggested further reading, so it works as a reference on the second pass as well as a syllabus on the first.
What the course covers
Fundamentals of AI and ML
Module 1 · 20%The core vocabulary: AI versus ML versus deep learning versus generative and agentic AI; supervised, unsupervised, and reinforcement learning; classification, regression, clustering, and anomaly detection; overfitting, bias, and evaluation metrics such as precision, recall, and F1; inferencing from batch to real time; the ML development lifecycle and MLOps; and the question the exam keeps asking — whether AI is the right tool at all. Ends with service-to-use-case mapping across SageMaker, Comprehend, Transcribe, Textract, Rekognition, Personalize, Forecast, and Lex.
Fundamentals of Generative AI
Module 2 · 24%How foundation models work and what they are good for: tokens, embeddings, context windows, transformers, and diffusion models; multimodality; inference parameters including temperature, top-p, and top-k; hallucination, nondeterminism, and the limits of interpretability; and the foundation model lifecycle. Covers the AWS generative-AI stack — Amazon Bedrock, Amazon Q Business and Q Developer, SageMaker JumpStart, and PartyRock — with the on-demand versus provisioned-throughput cost trade-off you are expected to reason about.
Applications of Foundation Models
Module 3 · 28%The heaviest and most technical module. Model-selection criteria; prompt engineering from zero-shot and few-shot to chain-of-thought and negative prompting; prompt attacks — injection, jailbreaking, and leaking; Retrieval Augmented Generation with Bedrock Knowledge Bases and vector stores such as OpenSearch and Aurora pgvector; the customization spectrum running from prompting through RAG and fine-tuning to continued pre-training; Bedrock Agents, Guardrails, and Prompt Management; and evaluation with ROUGE, BLEU, BERTScore, human review, and business-alignment metrics.
Guidelines for Responsible AI
Module 4 · 14%Fairness, the sources of bias and what dataset representativeness means in practice, transparency and explainability, veracity, safety, and human oversight — then the tooling that turns those principles into something auditable: SageMaker Clarify for bias detection and explainability, Amazon A2I for human review, SageMaker Model Cards for documentation, and AWS AI Service Cards for provider transparency. The exam asks these as scenarios about interpretable-versus-accurate model trade-offs and the legal exposure created by hallucinated content.
Security, Compliance, and Governance for AI Solutions
Module 5 · 14%Securing AI systems and governing the data behind them: IAM least privilege, KMS encryption, PrivateLink and VPC endpoints for private model access, Macie for sensitive-data discovery, CloudTrail and Bedrock model invocation logging for auditability, and where the shared responsibility model draws its line. Governance covers data lineage and cataloging, retention, vendor due diligence, AWS Artifact, AWS Config, Audit Manager, and the external frameworks the exam names — the NIST AI RMF and ISO/IEC 42001.
How to use it
Read the modules in order rather than skipping to the heavy ones. AIF-C01 is a vocabulary exam before it is anything else, and modules 2 and 3 assume the definitions module 1 sets up — you cannot reason about whether RAG or fine-tuning fits a scenario until inference, embeddings, and context windows are settled. Once a module is read, test it before moving on: the difference between recognising the phrase “chain-of-thought prompting” and picking it correctly out of four plausible options in a business scenario is the difference this exam is built to find.
For exam logistics — question count, timing, the scaled cut score, pricing, and how long the credential stays valid — and for the domain-weighted practice exam that pairs with these modules, see the AIF-C01 certification guide. If you want depth rather than breadth after this course, the MLA-C01 training course is the associate-level step that takes the same subject matter into building and operating production ML systems.
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Frequently asked questions about the AIF-C01 training course
Is the AIF-C01 training course free?
Yes. The course is free to read and requires no signup or account. It is funded by the same practice-exam catalogue it sits alongside.
How is the course structured?
One module per official AIF-C01 exam domain, in the order AWS publishes them, with each module weighted to the domain’s published percentage. Within each module the material is broken into short lessons, followed by key terms and further reading.
Does this replace AWS’s official training?
No. AWS publishes the official AIF-C01 exam guide and free digital courses on AWS Skill Builder, and those are the authoritative sources. This course is an independent study companion, written to be read quickly and to slot alongside practice questions — not a substitute for the official exam guide.
Do I need AI experience before starting?
The exam assumes up to six months of exposure to AI and ML technologies on AWS and familiarity with core services such as Amazon S3, AWS Lambda, and Amazon SageMaker. It does not assume you have trained a model. If AWS itself is new to you, read the CLF-C02 cloud practitioner course first, since AIF-C01 takes the underlying cloud vocabulary for granted.
What should I do after finishing the course?
Move to domain-weighted practice questions and work until you are clearing every domain consistently rather than carrying a weak module on the strength of the 28% one. The real exam is 65 questions in 90 minutes, so practise at that pace. After the exam, the natural next step is the associate-level MLA-C01 machine learning engineer course.
Is the course current with the latest AIF-C01 exam guide?
The course is written against the current exam guide, including the 2026 refresh that added agentic AI concepts — Amazon Bedrock Agents and the AgentCore runtime, Bedrock Data Automation, Prompt Management, and business-alignment metrics such as task completion rate and cost per interaction. AWS can revise the guide at any time; check the published exam guide before you book.
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All course content, questions, answers, and explanations on Certifym are original content created for study purposes. They are not actual AWS training materials or examination questions and are not represented as such. Studying with these materials does not guarantee a passing result on any live certification exam. Exam requirements, format, domain weights, pricing, and recertification policies are set by AWS and may change; always verify current details in the published AWS exam guide before scheduling your exam.