Overview

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The AWS Certified AI Practitioner (AIF-C01) exam doesn't reward memorisation. It tests your ability to apply AWS AI services to real-world scenarios — selecting the right tool, for the right reason, in the right context. This course is built around that distinction. Spread across all three modules, a 40-question video walkthrough solves exam-style questions on screen, step by step. Each explanation focuses on the reasoning behind the correct answer — why the right option works and why the distractors don't — so you build the kind of transferable exam logic that holds up under time pressure. Three focused modules consolidate your knowledge across the key AIF-C01 domain areas. Module 1 covers Generative AI on AWS — Amazon Bedrock, RAG, PII redaction, and the shared responsibility model for AI security. Module 2 addresses the ML lifecycle in production — SageMaker Ground Truth for data labeling at scale, model monitoring and performance drift, explainability, governance, and selecting the right performance metrics (Recall vs. Precision) based on business impact. Module 3 sharpens architectural decision-making — when to use RAG vs. fine-tuning, how to evaluate Foundation Models by cost, latency, and accuracy, and how to implement Intelligent Document Processing (IDP) workflows using Amazon Textract. Two full-length mock exams, accessible via external links in Module 3, simulate the real certification experience and help you confirm your readiness before exam day. Who this is for: AWS practitioners with foundational cloud knowledge who are preparing to earn the AIF-C01 credential. Prior experience with AWS services is assumed. This is an exam preparation course, not an introductory AWS AI course.

Syllabus

AI Fundamentals and Generative AI on AWS
- This module introduces the core concepts of Generative AI and the architectural patterns used to deploy them on AWS. You will explore how Amazon Bedrock serves as a foundation for building secure applications, how to protect sensitive data with PII redaction, and how to mitigate common AI risks like hallucinations using Retrieval-Augmented Generation (RAG).
ML Lifecycle Management and Responsible AI
- Moving beyond basic implementation, this module focuses on the "Care and Feeding" of machine learning models in a production environment. You will learn to navigate the ML lifecycle by labeling data at scale, monitoring for performance drift, and tuning hyperparameters like temperature to ensure consistent, high-quality model outputs.
Strategic AI Selection and Implementation Patterns
- The final module prepares you to make high-level architectural decisions by comparing different AI strategies and tools. You will learn the technical criteria for choosing between RAG and Fine-tuning, how to conduct side-by-side model evaluations on Bedrock, and how to automate complex data extraction from structured and unstructured documents.