Comprehensive AWS Certified AI Practitioner AIF-C01 Bootcamp
Master AIF-C01 & Pass on Your First Attempt | 2 Practice Exams + 330 Questions with Detailed Explanations
Lo que serás capaz de hacer
This course contains the use of artificial intelligence. Welcome. This course takes you from no AI background to exam-ready for the AWS Certified AI Practitioner (AIF-C01) certification. My name is Vladimir Raykov, and I will be your instructor. I earned the AWS Certified AI Practitioner certification in November 2024 and the AWS Certified Generative AI Developer Professional in December 2025, and I hold the AWS Early Adopter badge for both, awarded to the first 5,000 people worldwide to pass. I hold more than 10 cloud and AI certifications in total, including the Google Cloud Professional Cloud Architect. I have spent the last 10 years teaching online and have helped more than 200,000 students. Now I am here to help you get certified. Updated for the latest exam guide AWS revised the AIF-C01 exam guide, and this course is updated to match it. New lectures cover agentic AI, how AI agents use tools and memory, Model Context Protocol, token-based pricing, context engineering, and grounding techniques that keep model output accurate. The newly in-scope services are covered too: Amazon Bedrock AgentCore, Strands Agents, Kiro, Amazon Quick, and AWS Transform. Questions on services that left the exam scope have been replaced. By the end of the course, you will be ready to sit the official AWS Certified AI Practitioner exam. You will have a strong foundation in AI, machine learning, deep learning, and agentic AI, explained simply and clearly. More than 300 slides with diagrams and images support that. You will understand the AWS AI services the exam tests, including Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker AI, and pre-trained services such as Comprehend, Rekognition, and Textract. You will learn how AI is applied in real business scenarios and how to judge when AI is the right choice. And you will be ready for the exam's scenario-based questions, because you work through practical examples all the way through the course. Course structure: The course has 18 structured sections, aligned with the five exam domains: fundamentals of AI and ML, fundamentals of generative AI, applications of foundation models, guidelines for responsible AI, and security, compliance and governance of AI solutions. You get more than 160 bite-sized video lessons, around 17 hours in total. Every video is scripted for clear, concise delivery, with no filler and no thinking pauses. You also get more than 330 practice questions with detailed explanations, included as quizzes at the end of each section, and two full-length mock exams of 65 questions each that mirror the real exam format. A downloadable PDF summarizes the key takeaways for last-minute revision. The course is updated regularly as AWS services and exam content change. This course is designed for anyone looking to earn the AWS Certified AI Practitioner (AIF-C01) certification and add it to their professional toolkit. No prior AI or cloud experience required. Whether you want to understand how AI works in real business settings or you are preparing for your next role, this course will give you the knowledge and the confidence to pass the exam. It's perfect for: This course is for anyone looking to earn the AWS Certified AI Practitioner (AIF-C01) certification. No prior AI or cloud experience is required. It suits business analysts, IT support professionals, marketing professionals, product managers, project managers, team leads, IT managers, sales professionals, and anyone curious about AI and AWS. By the end, you will not only be prepared to pass the exam. You will understand the concepts behind it. Ready to get started? Watch the preview videos, especially "Roadmap to Success", to see my strategy for helping you pass the exam and truly understand the material. Click enroll, and let's start your AWS AI journey together. See you inside! This course is not affiliated with, endorsed by, or sponsored by Amazon Web Services (AWS) or Google Cloud Platform (GCP). AWS and Google Cloud are trademarks of their respective owners. All logos and trademarks are used for educational and identification purposes only.
- Comprehensive Preparation For AWS Certified AI Practitioner (AIF-C01) Certification: 17h High-Quality Video Content + A Total Of 450 Questions & Explanations.
- [Up-To-Date] Master The AIF-C01 Exam - No Previous Knowledge Needed.
- [Downloadable] Recap Of Key Concepts - PDF file (119 Pages).
- Explain what an AI agent is, how it uses tools and memory, and when an agent beats plain automation.
- Identify when to use Amazon Bedrock AgentCore, Strands Agents, and Kiro to build and run agents on AWS.
- Describe Model Context Protocol (MCP) and how multiple agents divide work between them.
Temario
UNIDAD 1 - Course Introduction · 18 leccionesUNIDAD 1 - Course Introduction
UNIDAD 1 - Course Introduction
- The Goal Of This Course And My Teaching Methodology
- AIF-C01 Exam Details
- [Roadmap To Success] Get AIF-C01 Certified - Part 1
- [Roadmap To Success] Get AIF-C01 Certified - Part 2
- [Fast Learning] Download The Most Important Points In PDF Slides
- Your Experience With This Course
- Key AWS Services And Concepts - Part 1
- Key AWS Services And Concepts - Part 2
- Artificial Intelligence vs Machine Learning vs Deep Learning - Part 1
- Artificial Intelligence vs Machine Learning vs Deep Learning - Part 2
- Artificial Intelligence vs Machine Learning vs Deep Learning - Part 3
- Agentic AI, The Newest Layer
- AI vs Machine Learning: What's the Difference?
- Please, Introduce Yourself
- Neural Networks Overview
- Computer Vision and Natural Language Processing (NLP)
- [Section Recap] AWS Key Concepts & AI vs ML vs DL
- [Quiz] AWS Key Concepts & AI vs ML vs DL
UNIDAD 2 - Mastering the Basics: AI & ML Concepts · 16 leccionesUNIDAD 2 - Mastering the Basics: AI & ML Concepts
UNIDAD 2 - Mastering the Basics: AI & ML Concepts
- Intro + The ML Process
- Data Types in Machine Learning
- Learning Types - Supervised Learning - Part 1
- Learning Types - Supervised Learning - Part 2
- Learning Types - Unsupervised Learning - Part 1
- Learning Types - Unsupervised Learning - Part 2
- Learning Types - Reinforcement Learning
- Learning Types - Semi-Supervised Learning
- What Is Inference? - Part 1
- What Is Inference? - Part 2
- [Practice Question] Inference And Deployment Options
- Value-Adding Artificial Intelligence (AI) Applications
- When Not to Use Artificial Intelligence (AI)
- Which Kind Of Model Does This Problem Need
- [Section Recap] Mastering the Basics: AI & ML Concepts
- [Quiz] Mastering the Basics: AI & ML Concepts
UNIDAD 3 - AWS AI Managed Services Deep Dive · 24 leccionesUNIDAD 3 - AWS AI Managed Services Deep Dive
UNIDAD 3 - AWS AI Managed Services Deep Dive
- Intro
- Amazon Rekognition
- Amazon Rekognition - Demo
- Amazon Transcribe
- Amazon Transcribe - Demo
- Amazon Translate
- Amazon Translate - Demo
- Amazon Comprehend
- Amazon Comprehend - Demo
- Amazon Lex
- Amazon Lex - Demo
- Amazon Polly
- Amazon Polly - Demo
- Amazon Fraud Detector
- Amazon Personalize
- Amazon Kendra
- Amazon Textract
- Amazon Textract - Demo
- Amazon Forecast
- Amazon Mechanical Turk (MTurk)
- Amazon Augmented AI (A2I)
- Amazon Augmented AI (A2I) - Demo
- [Section Recap] AWS AI Managed Services Deep Dive
- [Quiz] AWS AI Managed Services Deep Dive
UNIDAD 4 - Amazon SageMaker AI Essentials And The ML Development Lifecycle · 19 leccionesUNIDAD 4 - Amazon SageMaker AI Essentials And The ML Development Lifecycle
UNIDAD 4 - Amazon SageMaker AI Essentials And The ML Development Lifecycle
- Intro
- Amazon SageMaker AI Overview - Part 1
- Amazon SageMaker AI Overview - Part 2
- Machine Learning Development Lifecycle (Phases 1-3)
- Machine Learning Development Lifecycle (Phase 4A)
- Machine Learning Development Lifecycle (Phase 4B)
- Machine Learning Development Lifecycle (Phases 5-7)
- Machine Learning Development Lifecycle (Recap)
- ML Development Lifecycle vs ML Pipeline
- MLOps Fundamentals
- Mapping the AWS SageMaker AI Tools To The ML Pipeline Components - Part 1
- Mapping the AWS SageMaker AI Tools To The ML Pipeline Components - Part 2
- Amazon SageMaker AI - Model Sources And Selection
- Technical Performance Metrics - For Classification Problems
- Technical Performance Metrics - For Regression Problems
- Business Performance Metrics
- [Section Recap] Amazon SageMaker AI Essentials And The ML Development Lifecycle
- [Quiz 1] Amazon SageMaker AI Essentials And The ML Development Lifecycle
- [Quiz 2] Amazon SageMaker AI Essentials And The ML Development Lifecycle
UNIDAD 5 - Navigating Generative AI: Core Components, Model Types and Lifecycle · 15 leccionesUNIDAD 5 - Navigating Generative AI: Core Components, Model Types and Lifecycle
UNIDAD 5 - Navigating Generative AI: Core Components, Model Types and Lifecycle
- Intro
- Foundation Models (FMs) - Overview
- Large Language Models (LLMs) - Overview
- Tokens, Embeddings, and Vectors - Part 1
- Tokens, Embeddings, and Vectors - Part 2
- What Your Tokens Actually Cost
- Multimodal Models
- Diffusion Models
- The Foundation Model Lifecycle (Phases 1-3)
- The Foundation Model Lifecycle (Phases 4-7)
- What Makes A System An Agent
- How An Agent Uses Tools And Memory
- How Agents Connect To Tools And Other Agents
- [Section Recap] Navigating Generative AI: Core Components, Model Types & Life...
- [Quiz] Navigating Generative AI: Core Components, Model Types and Lifecycle
UNIDAD 6 - Business Applications of Generative AI · 7 leccionesUNIDAD 6 - Business Applications of Generative AI
UNIDAD 6 - Business Applications of Generative AI
- Intro
- Advantages of Generative AI
- Disadvantages of Generative AI
- Model Selection Factors For Generative AI
- Business Value Assessment
- [Section Recap] Business Value Assessment
- [Quiz] Business Applications of Generative AI
UNIDAD 7 - Your GenAI Toolkit: Essential AWS Services and Features · 29 leccionesUNIDAD 7 - Your GenAI Toolkit: Essential AWS Services and Features
UNIDAD 7 - Your GenAI Toolkit: Essential AWS Services and Features
- Intro
- Why Should You Use the AWS Infrastructure for GenAI?
- Amazon Bedrock Overview
- [Updated] Amazon Bedrock - Request Model Access - Demo
- Amazon Bedrock - Playgrounds - Demo
- Amazon Bedrock - Discover Menu - Walkthrough - Part 1
- Amazon Bedrock - Discover Menu - Walkthrough - Part 2
- Amazon Bedrock - Test Menu - Walkthrough
- Amazon Bedrock - Infer Menu - Walkthrough - Part 1
- Amazon Bedrock - Infer Menu - Walkthrough - Part 2
- Amazon Bedrock - Tune Menu - Walkthrough - Part 1
- Amazon Bedrock - Tune Menu - Walkthrough - Part 2
- Amazon Bedrock - Tune Menu - Walkthrough - Part 3
- Amazon Bedrock - Build Menu - Walkthrough - Part 1
- Amazon Bedrock - Build Menu - Walkthrough - Part 2
- Amazon Bedrock - Build Menu - Walkthrough - Part 3
- Amazon Bedrock - Build Menu - Walkthrough - Part 4
- Amazon Bedrock - Assess Menu - Walkthrough
- Amazon Bedrock - Configure and Learn - Walkthrough
- PartyRock Overview
- PartyRock Demo
- Amazon SageMaker JumpStart
- Where Each AWS Agent Service Fits
- [Kept For Reference] Amazon Q Business and Amazon Q Developer
- From Amazon Q To Amazon Quick
- Connect Your Knowledge - The AI/ML Stack
- Cost Considerations for AWS AI/LM Services - 6 Key Factors
- [Section Recap] Your GenAI Toolkit: Essential AWS Services and Features
- [Quiz] Your GenAI Toolkit: Essential AWS Services and Features
UNIDAD 8 - Applications of Foundation Models: Key Design Considerations · 11 leccionesUNIDAD 8 - Applications of Foundation Models: Key Design Considerations
UNIDAD 8 - Applications of Foundation Models: Key Design Considerations
- Intro
- What Factors Should You Consider When Choosing a Foundation Model?
- Understand Inference Parameters - Temperature, Top K, Top P, Input/Output Length
- What Is Retrieval Augmented Generation (RAG)?
- Amazon Bedrock - RAG and Knowledge Bases
- Amazon Bedrock - RAG Demonstration
- Vector Database Options For Storing Embeddings
- Foundation Model Customization Methods - Cost and Implementation
- What AI Agents Do For A Business
- [Section Recap] Applications of Foundation Models: Key Design Considerations
- [Quiz] Applications of Foundation Models: Key Design Considerations
UNIDAD 9 - Prompt Engineering Essentials and AI Vulnerabilities · 10 leccionesUNIDAD 9 - Prompt Engineering Essentials and AI Vulnerabilities
UNIDAD 9 - Prompt Engineering Essentials and AI Vulnerabilities
- Intro
- What Is Prompt Engineering?
- Prompt Engineering Techniques: Building on the Basics
- Optimizing Prompts for Quality and Specificity
- Where Your Prompts Should Live
- What Context Engineering Adds To Prompt Engineering
- AI Vulnerabilities: Exposure, Poisoning, Hijacking, Prompt Injection
- AI Vulnerabilities: Jailbreaking
- [Section Recap] Prompt Engineering Essentials and AI Vulnerabilities
- [Quiz] Prompt Engineering Essentials and AI Vulnerabilities
UNIDAD 10 - Fine-Tuning Foundation Models (FMs) - Deep Dive · 6 leccionesUNIDAD 10 - Fine-Tuning Foundation Models (FMs) - Deep Dive
UNIDAD 10 - Fine-Tuning Foundation Models (FMs) - Deep Dive
- Intro
- Methods For Fine-Tuning a Foundation Model - 1
- Methods For Fine-Tuning a Foundation Model - 2
- Data Preparation For Fine-Tuning a Foundation Model
- [Section Recap] Fine-Tuning Foundation Models (FMs) - Deep Dive
- [Quiz] Fine-Tuning Foundation Models (FMs) - Deep Dive
UNIDAD 11 - Evaluating Foundation Models: Methods and Metrics · 7 leccionesUNIDAD 11 - Evaluating Foundation Models: Methods and Metrics
UNIDAD 11 - Evaluating Foundation Models: Methods and Metrics
- Intro
- Discover the Different Approaches to Evaluate Foundation Models - 1
- Discover the Different Approaches to Evaluate Foundation Models - 2
- FM Eval. Metrics: Perplexity, BLUE, ROUGE, BERTscore, Accuracy, and F1-Score - 1
- FM Eval. Metrics: Perplexity, BLUE, ROUGE, BERTscore, Accuracy, and F1-Score - 2
- [Section Recap] Evaluating Foundation Models: Methods and Metrics
- [Quiz] Evaluating Foundation Models: Methods and Metrics
UNIDAD 12 - Responsible AI Development: Key Concepts And Considerations · 10 leccionesUNIDAD 12 - Responsible AI Development: Key Concepts And Considerations
UNIDAD 12 - Responsible AI Development: Key Concepts And Considerations
- Intro
- Responsible AI - Key Concepts
- Exploring Guardrails in Amazon Bedrock: A Practical Demonstration
- Responsible Model Selection: Environmental and Sustainability Considerations
- Generative AI: Legal and Ethical Concerns
- Balance - An Essential Characteristic of High-Quality Datasets
- Model Fit: Bias and Variance (Underfitting and Overfitting) - 1
- Model Fit: Bias and Variance (Underfitting and Overfitting) - 2
- [Section Recap] Responsible AI Development: Key Concepts And Considerations
- [Quiz] Responsible AI Development: Key Concepts And Considerations
UNIDAD 13 - Making AI Understandable · 7 leccionesUNIDAD 13 - Making AI Understandable
UNIDAD 13 - Making AI Understandable
- Intro
- AWS AI Service Cards: What, Why, and an Example
- SageMaker Model Cards
- AWS SageMaker Clarify
- The 3 Key Principles Of Human-Centered Design For Responsible AI
- [Section Recap] Making AI Understandable
- [Quiz] Making AI Understandable
UNIDAD 14 - Essential Security Practices and Tools on AWS · 9 leccionesUNIDAD 14 - Essential Security Practices and Tools on AWS
UNIDAD 14 - Essential Security Practices and Tools on AWS
- Intro
- AI System Security & The AWS Shared Responsibility Model
- Identity and Access Management - Users, Groups, Roles, Policies, Permissions - 1
- Identity and Access Management - Users, Groups, Roles, Policies, Permissions - 2
- AWS Encryption Capabilities (Data-at-Rest and Data-in-Transit)
- Network Security for AI Workloads (AWS PrivateLink)
- Keeping Model Output Grounded
- [Section Recap] Essential Security Practices and Tools on AWS
- [Quiz] Essential Security Practices and Tools on AWS
UNIDAD 15 - Data Security and Governance for AI Systems · 7 leccionesUNIDAD 15 - Data Security and Governance for AI Systems
UNIDAD 15 - Data Security and Governance for AI Systems
- Intro
- Secure Data Engineering Best Practices - Part 1
- Secure Data Engineering Best Practices - Part 2
- Data Provenance and Lineage
- Data Governance Strategies
- [Section Recap] Data Security and Governance for AI Systems
- [Quiz] Data Security and Governance for AI Systems
UNIDAD 16 - AI Security, Compliance, and Governance · 7 leccionesUNIDAD 16 - AI Security, Compliance, and Governance
UNIDAD 16 - AI Security, Compliance, and Governance
- Intro
- Security and Privacy Considerations for AI Systems
- Regulatory Compliance Standards for AI Systems
- AWS Services for Governance and Compliance
- Governance Protocols and Frameworks for Generative AI
- [Section Recap] AI Security, Compliance, and Governance
- [Quiz] AI Security, Compliance, and Governance
UNIDAD 17 - Practice Exams · 2 leccionesUNIDAD 17 - Practice Exams
UNIDAD 17 - Practice Exams
- AIF-C01 - Practice Exam #1
- AIF-C01 - Practice Exam #2
UNIDAD 18 - Exam Tips & Final Words · 3 leccionesUNIDAD 18 - Exam Tips & Final Words
UNIDAD 18 - Exam Tips & Final Words
- 8 Key Strategies for Success - Pass AIF-C01
- Final Words
- Bonus Lecture
Tu certificado lo emite la SEP, no solo una plataforma
Al demostrar la competencia recibes un certificado emitido por la Secretaría de Educación Pública a través del Gobierno del Estado de México: un documento oficial que puedes presentar con confianza ante cualquier empleador o institución.
Tu certificado con INEC
- Lo emite la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México
- Documento oficial con folio verificable en línea
- Lo otorga la Universidad Tecnológica Fidel Velázquez
Otras plataformas en línea
- Constancia privada emitida por la misma plataforma
- Solo acredita horas cursadas o asistencia
- Sin respaldo de una autoridad educativa
Tu itinerario, de la inscripción al certificado
- 1
Inscripción con pago único
Pagas $2,800 MXN una sola vez y entras de inmediato al curso.
- 2
Estudias a tu ritmo
18 unidades con videos, lecturas y actividades, disponibles 24/7 durante 12 meses.
- 3
Cuestionarios por unidad
19 cuestionarios para comprobar lo aprendido.
- 4
Certificado
Emitido por la SEP a través del Gobierno del Estado de México, con folio verificable.
Un certificado oficial de la SEP
- Emitido por la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México. No es una constancia privada: es un documento oficial, y ese respaldo marca la diferencia frente a otras plataformas en línea.
- Contenido impartido por Vladimir Raykov.
- Folio y QR verificables para compartirlo con quien te contrate.
Empieza hoy, a tu ritmo
Un solo pago y acceso inmediato al curso completo durante 12 meses.
- 18 unidades · 207 lecciones · 60 h
- 12 meses de acceso
- 19 cuestionarios de evaluación
- Certificado emitido por la SEP (Gob. del Estado de México) con folio verificable
Sobre tu inscripción
¿Cómo es el pago?
Un solo pago de $2,800 MXN. No hay mensualidades ni cargos recurrentes.
¿Cuánto tiempo tengo acceso?
12 meses desde tu inscripción, 24/7, para avanzar a tu ritmo.
¿Quién imparte el contenido?
El curso es impartido por Vladimir Raykov; INEC gestiona tu inscripción, evaluación y certificado.
¿Qué pasa cuando vence mi acceso?
Puedes renovar tu cuenta tú mismo desde la plataforma, sin trámites ni esperas.


