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AWS AIF-C01 Study Guide

AWS Certified AI Practitioner AIF-C01

The AWS Certified AI Practitioner (AIF-C01) is AWS's foundational exam for artificial intelligence. It covers the basics of AI, machine learning and generative AI, how foundation models are used, responsible AI, and security and governance. You do not need to build models or write code to take it.

  • Exam code AIF-C01
  • Foundational level
  • 5 exam domains

This guide is free, with no sign-up. It explains every AWS AIF-C01 exam topic in plain English, one chapter at a time. Read them in order or jump straight to the topic you need.

  • 5chapters
  • 33topics
  • 11diagrams and exhibits
  • 2 h 14 minfast track
Start with Fundamentals of AI and ML
  • Every topic follows the same simple pattern: what it is, how it works, common mistakes
  • Diagrams and real command output where a picture explains it best
  • Exam tips and worked subnetting and IPv6 practice, with answers

How the exam is weighted

Each domain is worth this share of the exam, so spend your study time in proportion. Select a domain to open its chapter.

Chapters

Open a chapter to see its topics and how long each takes.

1Fundamentals of AI and ML8 topics · 33 min fast trackCore terms, learning types, use cases, AWS AI services, the ML lifecycle, metrics and SageMaker AI.
  1. 1AI, ML and Deep Learning4 min
  2. 2Learning Types and Data Types4 min
  3. 3Model Fit, Bias and Inference5 min
  4. 4When to Use AI and ML4 min
  5. 5AWS Managed AI Services4 min
  6. 6The ML Development Lifecycle4 min
  7. 7Model Performance Metrics4 min
  8. 8SageMaker AI and MLOps4 min
Read the chapter overview →
2Fundamentals of GenAI7 topics · 28 min fast trackCore concepts, the model lifecycle, use cases, limits, model choice and AWS services for GenAI.
  1. 1Generative AI Core Concepts4 min
  2. 2Foundation Model Lifecycle4 min
  3. 3GenAI Use Cases4 min
  4. 4GenAI Capabilities and Limitations4 min
  5. 5Model Choice and Business Metrics4 min
  6. 6AWS Services for GenAI4 min
  7. 7AWS GenAI Advantages and Costs4 min
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3Applications of Foundation Models7 topics · 28 min fast trackModel selection, RAG, prompt engineering, fine tuning and how to evaluate FM output.
  1. 1FM Selection and Inference Settings4 min
  2. 2Retrieval Augmented Generation4 min
  3. 3Customisation Trade Offs and Agents4 min
  4. 4Prompt Engineering Techniques4 min
  5. 5Prompt Best Practices and Risks4 min
  6. 6FM Training and Fine Tuning4 min
  7. 7Evaluating FM Performance4 min
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4Guidelines for Responsible AI6 topics · 24 min fast trackFairness, dataset bias, bias detection tools, GenAI legal risks, and transparent, explainable models.
  1. 1Features of Responsible AI4 min
  2. 2Datasets, Bias and Variance4 min
  3. 3Detecting Bias and Trustworthiness4 min
  4. 4GenAI Legal Risks and Guardrails4 min
  5. 5Transparent and Explainable Models4 min
  6. 6Explainability Tools and Human Centered Design4 min
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5Security, Compliance and Governance5 topics · 21 min fast trackOverview of the AIF-C01 chapter on securing AI, protecting data, meeting compliance standards and setting up governance with AWS services and tools.
  1. 1Securing AI with AWS Services4 min
  2. 2Secure Data Engineering and Lineage4 min
  3. 3AI Security Threats and Protection4 min
  4. 4Compliance Standards and AWS Tools4 min
  5. 5AI Governance and Data Policies5 min
Read the chapter overview →

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