The Integrated AWS Certified AI Practitioner (AIF-C01) Study Guide [75 Minute Read]

A First-Principles Approach to Understanding AI, Generative AI, and Their Practical Application on AWS

Welcome to 'The Integrated AWS Certified AI Practitioner (AIF-C01) Study Guide.' This guide is meticulously crafted to help you build a deep, practical understanding of fundamental Artificial Intelligence (AI), Machine Learning (ML), and Generative AI concepts and their application on AWS. You will build knowledge from foundational truths, understanding the 'why' behind every service choice and its role in solving real-world business problems.

This guide is structured into digestible, focused learning blocks, each designed to deliver a specific piece of knowledge. Every topic is aligned with the official AWS AIF-C01 exam objectives, targeting the 'understand, identify, and determine' cognitive level required for success. Prepare to understand AI/ML technologies, determine their correct application, and use them responsibly to drive value in your organization.

---

(Table of Contents - For Reference)

• Phase 1: Fundamentals of AI and ML

  • 1.1. Explaining Basic AI Concepts and Terminologies
    • 1.1.1. 💡 First Principle: AI, ML, and Deep Learning
    • 1.1.2. 💡 First Principle: Supervised, Unsupervised, and Reinforcement Learning
    • 1.1.3. 💡 First Principle: Core Terminology (Model, Algorithm, Inferencing, Bias, Fairness)
    • 1.1.4. 💡 First Principle: Data for AI Models (Labeled, Unlabeled, Structured, Unstructured)
  • 1.2. Identifying Practical Use Cases for AI
    • 1.2.1. Recognizing Value and Appropriateness of AI/ML
    • 1.2.2. Matching ML Techniques to Use Cases (Regression, Classification, Clustering)
    • 1.2.3. Overview of AWS Managed AI/ML Services (Comprehend, Rekognition, etc.)
  • 1.3. Describing the ML Development Lifecycle
    • 1.3.1. Components of an ML Pipeline (From EDA to Monitoring)
    • 1.3.2. Understanding Model Sources (Pre-trained vs. Custom)
    • 1.3.3. Mapping AWS Services to the ML Pipeline (SageMaker, Data Wrangler, Model Monitor)
    • 1.3.4. Fundamental Concepts of MLOps and Performance Metrics

• Phase 2: Fundamentals of Generative AI

  • 2.1. Explaining the Basic Concepts of Generative AI
    • 2.1.1. 💡 First Principle: Foundation Models, LLMs, and Diffusion Models
    • 2.1.2. 💡 First Principle: Tokens, Embeddings, and Prompt Engineering
    • 2.1.3. The Foundation Model Lifecycle
  • 2.2. Understanding the Capabilities and Limitations of Generative AI
    • 2.2.1. Advantages of Generative AI (Adaptability, Simplicity)
    • 2.2.2. Disadvantages and Risks (Hallucinations, Inaccuracy, Nondeterminism)
    • 2.2.3. Factors for Selecting a Generative AI Model
  • 2.3. Describing AWS Infrastructure and Technologies for Generative AI
    • 2.3.1. Key AWS Services for Generative AI (Bedrock, SageMaker JumpStart, Amazon Q)
    • 2.3.2. Advantages and Cost Tradeoffs of AWS GenAI Services

• Phase 3: Applications of Foundation Models

  • 3.1. Describing Design Considerations for Applications
    • 3.1.1. Criteria for Choosing Pre-trained Models
    • 3.1.2. 💡 First Principle: Retrieval Augmented Generation (RAG)
    • 3.1.3. Vector Databases on AWS (OpenSearch, Aurora, etc.)
  • 3.2. Choosing Effective Prompt Engineering Techniques
    • 3.2.1. Concepts and Constructs of Prompt Engineering
    • 3.2.2. Zero-shot, Single-shot, and Few-shot Prompting
  • 3.3. Describing the Training and Fine-tuning Process
    • 3.3.1. Key Elements of Training and Fine-tuning
    • 3.3.2. Preparing Data for Fine-tuning
  • 3.4. Describing Methods to Evaluate Foundation Model Performance

• Phase 4: Guidelines for Responsible AI

  • 4.1. Explaining the Development of Responsible AI Systems
    • 4.1.1. 💡 First Principle: Pillars of Responsible AI (Bias, Fairness, Inclusivity, etc.)
    • 4.1.2. Tools for Responsible AI on AWS (SageMaker Clarify, Guardrails for Amazon Bedrock)
    • 4.1.3. Identifying Legal and Business Risks of Generative AI
  • 4.2. Recognizing the Importance of Transparent and Explainable Models
    • 4.2.1. Transparent vs. Opaque Models
    • 4.2.2. Tools for Transparency (SageMaker Model Cards)
    • 4.2.3. Human-centered Design for Explainable AI

• Phase 5: Security, Compliance, and Governance for AI Solutions

  • 5.1. Explaining Methods to Secure AI Systems
    • 5.1.1. Securing AI Systems with IAM, Encryption, and AWS PrivateLink
    • 5.1.2. The AWS Shared Responsibility Model for AI
    • 5.1.3. Data Lineage and Documentation (SageMaker Model Cards)
  • 5.2. Recognizing Governance and Compliance Regulations for AI
    • 5.2.1. Identifying Regulatory Standards (ISO, SOC)
    • 5.2.2. AWS Services for Governance and Compliance (AWS Config, CloudTrail, Audit Manager)
    • 5.2.3. Data Governance Strategies (Lifecycles, Residency, Retention)

• Phase 6: Exam Readiness & Beyond

  • 6.1. Exam Preparation Strategies
  • 6.2. Beyond the Exam: Continuous Learning & Community

• Phase 7: Glossary

---