This repo is a study guide for the AWS Certified AI Practitioner (AIF-C01) exam. It also covers how common AI and generative AI ideas show up on AWS in real projects.
The material follows the official exam guide (PDF, HTML hub). For exam scope, service names, and domain weights, rely on AWS docs and the guide; both can change.
- Professionals who need foundational AI/ML and generative AI literacy on AWS.
- Candidates preparing for AIF-C01 who want domain-by-domain coverage plus hands-on examples.
- Anyone mapping business problems to the right AWS AI building blocks (without assuming you will train models from scratch).
The exam’s target candidate uses AI/ML on AWS but is not expected to implement deep model engineering, heavy MLOps pipelines, or organization-wide governance frameworks. On the exam those topics show up as concepts to recognize, not as tasks to perform.
| Item | Detail |
|---|---|
| Exam code | AIF-C01 |
| Level | Foundational (AWS Certification) |
| Question types | Multiple choice, multiple response, ordering, matching |
| Scored questions | 50 (plus 15 unscored questions that do not affect your score) |
| Passing score | 700 on a scaled score of 100–1000 |
| Scoring model | Compensatory (overall pass; section weights differ) |
Recommended knowledge (from AWS): familiarity with core AWS services (for example EC2, S3, Lambda, Amazon Bedrock, Amazon SageMaker AI), the shared responsibility model, IAM, and pricing models. Up to about six months’ exposure to AI/ML on AWS is typical for the target candidate.
Out-of-scope job tasks (examples from AWS): developing model algorithms, heavy feature engineering, hyperparameter tuning, building full AI/ML pipelines or security/compliance programs. On the exam you need to recognize what these are, not perform them at expert depth.
| Domain | Topic | Weight |
|---|---|---|
| 1 | Fundamentals of AI and ML | 20% |
| 2 | Fundamentals of GenAI | 24% |
| 3 | Applications of Foundation Models | 28% |
| 4 | Guidelines for Responsible AI | 14% |
| 5 | Security, Compliance, and Governance for AI Solutions | 14% |
- Read the domain guides in order (1 through 5). Later domains assume vocabulary from earlier ones.
- Cross-link to AWS docs for anything operational (IAM, encryption, regional availability, pricing).
- Run the code examples under
examples/to connect API shapes to the concepts (Bedrock, boto3 patterns, evaluation and monitoring ideas). - Validate exam scope using the official in-scope services list.
| Phase | Focus | Activities |
|---|---|---|
| 1 | Vocabulary & lifecycle | Domain 1 guide; sketch one ML lifecycle for a business problem you know. |
| 2 | GenAI building blocks | Domain 2 guide; list 3 GenAI use cases and 2 failure modes (hallucination, cost). |
| 3 | FMs in production | Domain 3 guide; practice explaining RAG, agents, and evaluation metrics out loud. |
| 4 | Responsibility & trust | Domain 4 guide; map tools (Guardrails, Clarify, Model Monitor, A2I) to risks. |
| 5 | Security & governance | Domain 5 guide; trace IAM → encryption → logging for a Bedrock workload on paper. |
| 6 | Service mapping | guides/06-aws-services-primer.md; drill “which service for which scenario?” |
| 7 | Hands-on | Run Bedrock examples in a sandbox account; adjust inference parameters and observe changes. |
| Guide | File |
|---|---|
| Domain 1: AI & ML fundamentals | guides/01-fundamentals-ai-and-ml.md |
| Domain 2: Generative AI fundamentals | guides/02-fundamentals-of-genai.md |
| Domain 3: Foundation models in applications | guides/03-applications-of-foundation-models.md |
| Domain 4: Responsible AI | guides/04-responsible-ai.md |
| Domain 5: Security, compliance, governance | guides/05-security-compliance-governance.md |
| AWS services at a glance (exam-oriented) | guides/06-aws-services-primer.md |
| Example | Description |
|---|---|
| examples/bedrock_converse.py | Invoke a foundation model with the Converse API (messages, inference parameters). |
| examples/bedrock_embeddings.py | Generate embeddings for RAG-style workflows. |
| examples/rag_similarity_concept.py | Cosine similarity between vectors (RAG retrieval concept). |
| examples/requirements.txt | Minimal Python dependencies for the samples. |
Examples assume credentials via the default AWS credential chain (for example environment variables, ~/.aws/credentials, or an IAM role). Replace model IDs and regions with values valid for your account.
- AWS Certified AI Practitioner (certification home)
- Exam guide (AIF-C01) (domains, tasks, policies)
- Exam Prep on AWS Skill Builder (training aligned with AWS Certification)
- AWS Well-Architected (operational excellence, security, cost, sustainability)
This guide is educational and not affiliated with AWS. Exams, service names, and guides change; check AWS documentation before you schedule a test or design production systems.