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AWS Certified AI Practitioner Study Guide

Master foundational AWS AI/ML concepts, generative AI, and responsible practices to pass the AIF-C01 exam.

(AWS-AIF01.AE1) / ISBN : 979-8-90059-118-6
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About This Course

This guide isn't about theoretical perfection; it's about practical mastery for the AWS Certified AI Practitioner exam. We'll dissect core AI/ML, generative AI, and AWS services like Bedrock and SageMaker. Expect to grapple with real-world trade-offs in model selection, prompt engineering, and MLOps. You'll learn to identify suitable use cases, understand data types, and implement responsible AI, preparing you for the AIF-C01 exam's technical demands. This isn't just a study guide; it's a deep dive into what actually works and what doesn't in AWS AI.

Skills You’ll Get

  • AI/ML Fundamentals: Master core AI, ML, and Generative AI concepts, including data types, model predictions, tokens, embeddings, and the Transformer architecture. Understand the relationship and distinctions between these fields, recognizing their inherent limitations.
  • AWS AI/ML Service Application: Effectively utilize AWS AI and ML services like Amazon Bedrock, SageMaker, and their components for various real-world use cases, including understanding their optimal application and common failure points.
  • Prompt Engineering & Model Customization: Develop robust prompt engineering strategies for foundation models, understand inference parameters, and apply customization techniques like fine-tuning and pre-training, recognizing associated data processing challenges and trade-offs.
  • Responsible AI & MLOps: Implement responsible AI principles using AWS services like SageMaker Clarify and Bedrock Guardrails. Grasp MLOps phases, pipeline automation, and inference optimizations for large language models, including security, governance, and compliance considerations.
Download Course Outline

1

Preface

  • What Does This Course Cover?
  • Who Should Read This Course
2

Basic AI Concepts and Terminology

  • A Brief History of AI
  • Diving Deeper into Terms You Should Know
  • The Relationship Among AI, ML, and Deep Learning
  • Understanding Data Types in AI Models
  • Making Predictions Using Trained Models
  • Summary
  • Exam Essentials
3

Basic Concepts of Generative AI

  • A New Way to Interact with AI
  • From Text to Numbers: Tokens, Chunking, and Embeddings
  • The Transformer Architecture and Foundation Models
  • Beyond Text: Multi-modal Models
  • Prompt Engineering
  • The Upsides and Downsides of Gen AI
  • Summary
  • Exam Essentials
4

Applications of AI and ML in Real-World Use Cases

  • Key Trends in AI and ML Applications
  • Use Cases Unsuitable for AI and ML Applications
  • Choosing the Right ML Techniques for Different Use Cases
  • Summary
  • Exam Essentials
5

AWS AI and ML Services

  • An Overview of AWS Managed AI and ML Services
  • AWS AI Services
  • AWS ML Services
  • Summary
  • Exam Essentials
6

Model Selection and Prompt Engineering

  • Selecting the Right Foundation Model for Your Use Case
  • The Effect of Inference Parameters on Model Responses
  • Prompt Engineering
  • Summary
  • Exam Essentials
7

Generative AI Applications with RAG and Agents

  • Retrieval-Augmented Generation Workflow
  • Amazon Bedrock Knowledge Bases
  • Amazon Bedrock Agents
  • Summary
  • Exam Essentials
8

Model Customization and Evaluation

  • Overview of Customization Techniques
  • Pre-training Models: Building the Foundation
  • Fine-tuning
  • AWS Services for Pre-training and Fine-tuning
  • Data Processing
  • Model Evaluation
  • Summary
  • Exam Essentials
9

MLOps

  • MLOps Phases
  • MLOps Pipeline
  • Automating MLOps
  • SageMaker Inference
  • Inference Optimizations for Large Language Models
  • Summary
  • Exam Essentials
10

Implementing Responsible AI with AWS Services

  • Key Principles of Responsible AI
  • ML Governance with SageMaker AI
  • Amazon SageMaker Clarify
  • Amazon Bedrock Guardrails
  • Amazon Bedrock Evaluations
  • Summary
  • Exam Essentials
11

AI Security, Governance, and Compliance

  • Security of AI Systems
  • Data Governance Strategies
  • Compliance and Regulatory Frameworks in AI
  • Summary
  • Exam Essentials
12

Practice Test 1 

13

Practice Test 2

  • Question
14

Flashcard

  • Flashcard

1

Basic AI Concepts and Terminology

2

Basic Concepts of Generative AI

  • IntroductionIn this lab, you will understand how tokenization works in LLMs (Large Language Model...
  • Understanding Tokenization in LLM
  • Improving AI-Generated Product Descriptions Using Structured Prompts
3

Applications of AI and ML in Real-World Use Cases

4

AWS AI and ML Services

  • Exploring and Evaluating Foundation Models Using Amazon Bedrock
  • Enhancing Software Development Using Amazon Q Developer
  • Creating an App on AWS PartyRock
  • tth
  • Extracting Insights from Text Using Amazon Comprehend
  • Translating Language Using Amazon Translate
  • Analyzing Sample Document Using Amazon Textract
  • Building and Evaluating a Classification Model
  • Selecting the Best Classification Model
5

Model Selection and Prompt Engineering

  • IntroductionIn this lab, you will understand how...nd length based on specific use cases.Questions
  • Refining Prompts for an Edtech AI Assistant
6

Generative AI Applications with RAG and Agents

  • Creating and Testing a Basic Customer Support Agent
7

Model Customization and Evaluation

  • Creating a HyperPod Cluster
  • Evaluating LLM Performance Using SageMaker Clarify
8

MLOps

9

Implementing Responsible AI with AWS Services

  • Evaluating Model Responses Using LLM-as-a-Judge
10

AI Security, Governance, and Compliance

  • Monitoring and Detecting Misconfiguration with AWS Config

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We'll clarify their distinct roles and interdependencies, focusing on how each applies to AWS services and real-world problem-solving, not just theoretical definitions. Expect to understand where each technique offers value and where it falls short.

Passing the SCOR exam earns the Cisco Certified Specialist - Security Core title and counts toward CCNP/CCIE Security recertification.

Yes, it starts with basic AI concepts and terminology, building foundational knowledge before diving into AWS-specific services and advanced topics like generative AI. We assume you're an engineer, not necessarily an AI expert.

We explicitly cover use cases unsuitable for AI/ML, discussing data quality issues, ethical considerations, and the inherent trade-offs in model selection and deployment. Understanding limitations is as crucial as understanding capabilities.

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