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AIF-C01Free Study Guide

AWS Certified AI Practitioner AIF-C01The Complete Beginner's Guide

A structured learning curriculum mapping to all official exam objectives for the AWS Certified AI Practitioner certification.

16 chapters
~3 hours total read
Free — no signup required
By Johnson Ajibi · Senior Network & Security Engineer · MSc IT Security

How to use this guide

This guide works best as a loop: read a chapter, test yourself with practice questions, look up unfamiliar terms in the glossary, then move to the next chapter.

① Read a chapter② Answer practice questions③ Review missed answers④ Repeat
Study Chapters

16 chapters covering every exam objective. Each chapter includes key concepts, exam tips, common traps, comparison tables, and a 5-question quiz at the end.

Start Chapter 1
Practice Questions

Free timed and untimed practice with instant feedback and full explanations. Pick 10–120 questions per session. Filter by domain to drill your weak areas.

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Glossary

Every AIF-C01term defined and searchable. Use it when a chapter mentions a concept you haven't seen before or want a quick refresher on.

Browse glossary
Exam Overview

Exam blueprint, domain weights, passing score, duration, cost, and registration links. Start here if you're new to this certification.

View exam guide

Chapters — AIF-C01

1

Introduction to AI and ML on AWS

Objective 1.1 · Define AI, ML, and deep learning and understand their relationship

12m
2

Machine Learning Lifecycle and Basics

Objective 1.2 · Explain the ML lifecycle and key concepts such as training, inference, and evaluation

12m
3

AWS Machine Learning Services Overview

Objective 1.3 · Identify AWS services for AI/ML (Amazon SageMaker, Rekognition, Comprehend, Translate, etc.)

12m
4

Security, Governance, and Compliance for AI

Objective 2.1 · Understand security and compliance considerations for AI workloads on AWS (IAM, encryption, logging)

12m
5

AWS IAM and Data Protection for ML

Objective 2.2 · Apply IAM policies, encryption, and access controls to protect ML data and models

12m
6

Responsible AI Principles and AWS

Objective 3.1 · Explain responsible AI principles (fairness, explainability, privacy, robustness, transparency)

12m
7

Bias Detection and Mitigation in ML

Objective 3.2 · Identify and mitigate bias in AI/ML systems using AWS tools (SageMaker Clarify, etc.)

12m
8

AWS Tools for Governance and Auditability

Objective 3.3 · Use AWS services (AWS Audit Manager, CloudTrail, Config) for AI governance

18m
9

Generative AI Concepts and Foundation Models

Objective 4.1 · Define generative AI and foundation models, including large language models (LLMs)

12m
10

Prompt Engineering Techniques

Objective 4.2 · Apply prompt engineering best practices (zero-shot, few-shot, chain-of-thought)

13m
11

Amazon Bedrock Fundamentals

Objective 4.3 · Use Amazon Bedrock to access and deploy foundation models

12m
12

Fine-tuning and Customization of Foundation Models

Objective 4.4 · Customize foundation models using fine-tuning, RAG, and agent-based approaches

12m
13

Building with Amazon SageMaker

Objective 5.1 · Build, train, and deploy ML models using Amazon SageMaker

12m
14

AWS AI Services for Content and Vision

Objective 5.2 · Use AWS AI services (Rekognition, Textract, Comprehend, Polly, Lex) for vision and content tasks

12m
15

AWS AI Services for Language and Conversation

Objective 5.3 · Use AWS AI services (Translate, Transcribe, Lex, Polly) for language and conversational AI

12m
16

Deploying and Monitoring ML Models

Objective 5.4 · Deploy, monitor, and manage ML models in production using AWS tools (SageMaker, CloudWatch)

12m

Ready to test your knowledge?

Free AIF-C01 practice questions with full explanations. Test what you learn chapter by chapter.

AIF-C01 Practice Questions