https://github.com/khasky/aws-ai-practitioner
Everything you need to know to become AWS AI Practitioner
https://github.com/khasky/aws-ai-practitioner
ai aif-c01 aws exam guide practitioner study
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Everything you need to know to become AWS AI Practitioner
- Host: GitHub
- URL: https://github.com/khasky/aws-ai-practitioner
- Owner: khasky
- Created: 2026-03-22T05:59:33.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2026-03-22T06:11:25.000Z (2 months ago)
- Last Synced: 2026-03-22T21:12:39.719Z (2 months ago)
- Topics: ai, aif-c01, aws, exam, guide, practitioner, study
- Homepage:
- Size: 18.6 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Everything you need to know to become AWS AI Practitioner
This repo is a study guide for the **[AWS Certified AI Practitioner (AIF-C01)](https://aws.amazon.com/certification/certified-ai-practitioner/)** 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](https://docs.aws.amazon.com/pdfs/aws-certification/latest/ai-practitioner-01/ai-practitioner-01.pdf), [HTML hub](https://docs.aws.amazon.com/aws-certification/latest/ai-practitioner-01.html)). For exam scope, service names, and domain weights, rely on AWS docs and the guide; both can change.
---
## Who this is for
- 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.
---
## Exam snapshot (official basics)
| 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.
---
## Content domains and weights (scored content)
| 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%** |
---
## How to use this repo
1. **Read the domain guides in order** (1 through 5). Later domains assume vocabulary from earlier ones.
2. **Cross-link to AWS docs** for anything operational (IAM, encryption, regional availability, pricing).
3. **Run the code examples** under `examples/` to connect API shapes to the concepts (Bedrock, boto3 patterns, evaluation and monitoring ideas).
4. **Validate exam scope** using the official [in-scope services](https://docs.aws.amazon.com/aws-certification/latest/ai-practitioner-01/aif-01-in-scope-services.html) list.
### Suggested study sequence (example)
| 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 index
| Guide | File |
| ------------------------------------------- | ------------------------------------------------------------------------------------------------ |
| Domain 1: AI & ML fundamentals | [guides/01-fundamentals-ai-and-ml.md](guides/01-fundamentals-ai-and-ml.md) |
| Domain 2: Generative AI fundamentals | [guides/02-fundamentals-of-genai.md](guides/02-fundamentals-of-genai.md) |
| Domain 3: Foundation models in applications | [guides/03-applications-of-foundation-models.md](guides/03-applications-of-foundation-models.md) |
| Domain 4: Responsible AI | [guides/04-responsible-ai.md](guides/04-responsible-ai.md) |
| Domain 5: Security, compliance, governance | [guides/05-security-compliance-governance.md](guides/05-security-compliance-governance.md) |
| AWS services at a glance (exam-oriented) | [guides/06-aws-services-primer.md](guides/06-aws-services-primer.md) |
---
## Code examples
| Example | Description |
| ------------------------------------------------------------------------ | ------------------------------------------------------------------------------------- |
| [examples/bedrock_converse.py](examples/bedrock_converse.py) | Invoke a foundation model with the **Converse** API (messages, inference parameters). |
| [examples/bedrock_embeddings.py](examples/bedrock_embeddings.py) | Generate **embeddings** for RAG-style workflows. |
| [examples/rag_similarity_concept.py](examples/rag_similarity_concept.py) | **Cosine similarity** between vectors (RAG retrieval concept). |
| [examples/requirements.txt](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.
---
## Official resources
- [AWS Certified AI Practitioner](https://aws.amazon.com/certification/certified-ai-practitioner/) (certification home)
- [Exam guide (AIF-C01)](https://docs.aws.amazon.com/aws-certification/latest/ai-practitioner-01.html) (domains, tasks, policies)
- [Exam Prep on AWS Skill Builder](https://skillbuilder.aws/) (training aligned with AWS Certification)
- [AWS Well-Architected](https://aws.amazon.com/architecture/well-architected/) (operational excellence, security, cost, sustainability)
---
## Disclaimer
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.