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Kanani Nirav
Kanani Nirav

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AWS Certified AI Practitioner AIF-C01 Study Notes — A Study Guide

#aws #aifc01 #awsaipractitioner #awscertification

If you are preparing for AWS Certified AI Practitioner AIF-C01 Exam, this guide will help you with quick revision before the exam. I share the notes I used to study and pass exam.

Exam Study Notes Web Site Link: AWS Certified AI Practitioner Study Notes

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Test your knowledge with free practice Exams which will help you before the exam. Simply go through the below Link. you can see sample questions with the correct answers and detailed explanations or reference links.

Practice Exams: Practice Test

What is AWS Certified AI Practitioner (AIF-C01)?

The AWS Certified AI Practitioner (AIF-C01) exam validates overall knowledge of AI/ML, generative AI technologies, and associated AWS services and tools, independent of a specific job role. It is meant for people who use (but do not necessarily build) AI/ML solutions on AWS, with up to 6 months of exposure to AI/ML on AWS.

  • Questions: 65 (50 scored + 15 unscored)
  • Question types: Multiple choice, Multiple response, Ordering, Matching, Case study
  • Passing score: 700 (scaled score of 100–1,000)
  • No penalty for guessing: unanswered questions are scored as incorrect
Domain Weight
Domain 1: Fundamentals of AI and ML 20%
Domain 2: Fundamentals of Generative AI 24%
Domain 3: Applications of Foundation Models 28%
Domain 4: Guidelines for Responsible AI 14%
Domain 5: Security, Compliance, and Governance for AI Solutions 14%

Each Domain contains a number of sections, and each section contains a number of units. Below Table Link containing information about each sections in detail. Please feel free to comment if any information is inaccurate.

Table of contents

  1. AWS Certified AI Practitioner Study Guide

    • Introduction, Target candidate description, Exam content, Content outline (Domain 1 to Domain 5 task statements)

  2. Introduction to Cloud Computing and AWS

    • What is Cloud Computing?, The Five Characteristics of Cloud Computing, Six Advantages of Cloud Computing, Problems Solved by the Cloud

  3. GenAI Introduction

    • What is Generative AI?, What is Foundation Model?, Generative Language Models, GenAI for Images

  4. Amazon Bedrock

    • What is Amazon Bedrock?, Foundation Models, Fine-Tuning a Model, What is RAG (Retrieval-Augmented Generation)?, What is a Vector Database?, RAG vs Fine-Tuning Comparison, Amazon Bedrock Pricing

  5. Prompt Engineering

    • What is Prompt Engineering?, Negative Prompting, Prompt Performance Optimization, Prompt Latency, Prompt Engineering Techniques, Prompt Templates

  6. Amazon Q

    • Introduction to Amazon Q, Amazon Q Business, Amazon Q Apps, Amazon Q Developer

  7. AI and Machine Learning Overview

    • What is AI?, AI Components, What is Machine Learning (ML)?, What is Deep Learning (DL)?, What is the Transformer Model? (LLM), Diffusion Models, Multi-Modal Models, Training Data, Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, Self-Supervised Learning, Reinforcement Learning (RL), What is RLHF?, Model Fit, Bias and Variance, Model Evaluation Metrics, Confusion Matrix, AUC-ROC, Regression Metrics, Metrics for Evaluating LLMs, Inferencing, Phases of a Machine Learning Project, Hyperparameter Tuning, What to Do If the Model Is Overfitting?

  8. Introduction of AWS Managed AI Services

    • Why Use AWS AI Managed Services?, Examples of AWS AI Managed Services

  9. Amazon Comprehend

    • Amazon Comprehend Overview, Why Use Amazon Comprehend?, Custom Classification, Named Entity Recognition (NER), Custom Entity Recognition, Amazon Comprehend Medical

  10. Amazon Translate

    • Amazon Translate Overview

  11. Amazon Transcribe

    • Amazon Transcribe Overview, Improving Accuracy, Toxicity Detection, Amazon Transcribe Medical, Use Cases

  12. Amazon Polly

    • Overview, Lexicons, SSML format, Voice engine, Speech Marks

  13. Amazon Rekognition

    • Amazon Rekognition Overview, Rekognition Custom Labels, Content Moderation

  14. Amazon Lex

    • Amazon Lex Overview, Workflow

  15. Amazon Personalize

    • Amazon Personalize Overview

  16. Amazon Textract

    • Amazon Textract Overview

  17. Amazon Kendra

    • Amazon Kendra Overview, Key Concepts

  18. Amazon Mechanical Turk

    • Amazon Mechanical Turk Overview

  19. Amazon Augmented AI (A2I)

    • Amazon Augmented AI (A2I) Overview

  20. Hardware for AI

    • Amazon EC2, Amazon's Hardware for AI

  21. AWS Managed AI Services — Quick Revision Summary

    • Comprehend, Translate, Transcribe, Polly, Rekognition, Lex, Personalize, Textract, Kendra, Mechanical Turk, A2I, AI Hardware (Trainium & Inferentia)

  22. Amazon SageMaker

    • Amazon SageMaker Overview, Built-in ML Algorithms, Automatic Model Tuning (AMT), Model Deployment and Inference, SageMaker Model Deployment Comparison, SageMaker Studio, Data Wrangler, SageMaker Feature Store, SageMaker Clarify, SageMaker Ground Truth, ML Governance, SageMaker Model Dashboards, SageMaker Model Monitor, SageMaker Model Registry, SageMaker Pipelines, SageMaker JumpStart, SageMaker Canvas, MLFlow for Amazon SageMaker

  23. Responsible AI and Security

    • Core dimensions of responsible AI, AWS services for responsible AI, AWS AI Service Cards, Interpretability vs Explainability, High interpretability models – Decision Trees, Partial Dependence Plots (PDP), Human-Centered Design (HCD) for explainable AI

  24. GenAI Capabilities and Challenges

    • Capabilities of Generative AI, Challenges of Generative AI, Toxicity, Hallucinations, Plagiarism and Cheating, Prompt Misuses

  25. Compliance for AI

    • Regulated Workloads, AI Standard Compliance Challenges, AWS Compliance, Model Cards

  26. Governance for AI

    • Importance of Governance and Compliance, AI Governance Framework, AWS Tools Supporting AI Governance, Governance Strategies, Data Governance Strategies, Data Management Concepts, Data Lineage

  27. Security and Privacy for AI Systems

    • Monitoring AI systems, AWS Shared Responsibility Model, Secure Data Engineering – Best Practices

  28. MLOps (Machine Learning Operations)

    • MLOps Overview

  29. AWS Security Services and more

    • IAM — Identity and Access Management, Amazon S3, Amazon EC2, AWS Lambda, Amazon Macie, AWS Config, Amazon Inspector, AWS CloudTrail, AWS Artifact, AWS Audit Manager, AWS Trusted Advisor, VPC (Virtual Private Cloud), AWS Services for Bedrock

  30. Glossary of AWS AI Practitioner Exam

    • Core AI and ML Concepts, Learning Types, Model Training and Optimization, Model Evaluation Metrics, Model Behavior and Risks, Prompting Techniques, Data Concepts, Inference and Deployment, Retrieval and Vector Search, AWS AI and ML Services, Security, Governance and Pricing

Exam Study Notes Github Link: aws-certified-ai-practitioner-study-notes

AWS Cloud Practitioner Study Notes and Practice Exams (CLF-C02)

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