🤖 AWS Certified AI Practitioner (AIF-C01) — Exam-Prep

A free, independent exam-preparation course that walks through the published exam guide for the AWS Certified AI Practitioner (AIF-C01) — the fundamentals of AI/ML and generative AI, applications of foundation models, the core AWS AI services (Amazon Bedrock, SageMaker, Amazon Q), prompt-engineering basics, responsible AI, and security, compliance and governance — with rich visual lessons, original exam-style questions and a 10-question final exam (80% to pass). This is independent preparation only; it is not affiliated with, endorsed by, or the official training of Amazon Web Services. The course is organized into 9 modules, ending with a final exam (pass mark 70%). It is independent, free exam-preparation training — not an official or accredited review course.

What you'll learn

• Fundamentals of AI, ML & Deep Learning
• The ML Lifecycle, Model Types & Evaluation Metrics
• Fundamentals of Generative AI & Foundation Models
• Prompt Engineering, Tokens, Embeddings & Transformers
• The AWS AI/ML Stack: SageMaker, Bedrock & Managed AI Services
• Applying Foundation Models: RAG, Fine-Tuning, Agents, and Evaluation
• Responsible AI: Bias, Fairness, Transparency & Explainability
• Security, Governance & Compliance for AI Workloads
• Cost, Deployment & Monitoring of AI on AWS

Learning objectives

• Understand that this is independent exam-prep for AWS Certified AI Practitioner (AIF-C01), not the official AWS course
• Explain core AI, machine-learning and deep-learning concepts and tell supervised, unsupervised and reinforcement learning apart
• Describe generative AI, foundation models, large language models, tokens, embeddings and the transformer idea
• Recognise good real-world use cases for foundation models — and when classic ML or no ML is the better choice
• Navigate the AWS AI/ML stack: Amazon Bedrock, Amazon SageMaker (and SageMaker AI), Amazon Q and the managed AI services
• Apply prompt-engineering basics — zero/few-shot, instructions, context and inference parameters — and mitigate prompt risks
• Apply responsible-AI principles (fairness, explainability, transparency, robustness) using tools such as SageMaker Clarify and Bedrock Guardrails
• Reason about security, compliance and governance for AI on AWS (IAM, encryption, the shared responsibility model, data governance)
• Sit a 10-question exam-style final (80% to pass) built from original questions spanning the four exam domains