AWS-AIF01.AE1
AWS Certified AI Practitioner Study Guide
Master foundational AWS AI/ML concepts, generative AI, and responsible practices to pass the AIF-C01 exam.
- Practice in 18 Laboratorios prácticos — nothing to install
- 11 Lecciones interactivas y 64 topics mapped to the official exam objectives
- 312 Preguntas del examen de práctica y 2 Pruebas completas
Beginner A tu propio ritmo · 1 año de acceso
18 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
11Lecciones interactivas
64Topics
18Laboratorio en vivo
312Preguntas del examen de práctica
13Vídeos
135Tarjetas didácticas
50Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
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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.
- 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.
Course Highlights
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11 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
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18 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
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312 Preguntas de práctica Pruebas de evaluación con justificaciones de respuesta detalladas
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1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
11 Lecciones interactivas · 64 topics01 Preface 2 topics +
- What Does This Course Cover?
- Who Should Read This Course
02 Basic AI Concepts and Terminology 7 topics · 1 Laboratorio en vivo +
- 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
1 Laboratorio en vivo in this lesson — see the labs panel →
03 Basic Concepts of Generative AI 8 topics · 1 Laboratorio en vivo +
- 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
1 Laboratorio en vivo in this lesson — see the labs panel →
04 Applications of AI and ML in Real-World Use Cases 5 topics · 1 Laboratorio en vivo +
- 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
1 Laboratorio en vivo in this lesson — see the labs panel →
05 AWS AI and ML Services 5 topics · 8 Laboratorio en vivo +
- An Overview of AWS Managed AI and ML Services
- AWS AI Services
- AWS ML Services
- Summary
- Exam Essentials
8 Laboratorio en vivo in this lesson — see the labs panel →
06 Model Selection and Prompt Engineering 5 topics · 1 Laboratorio en vivo +
- Selecting the Right Foundation Model for Your Use Case
- The Effect of Inference Parameters on Model Responses
- Prompt Engineering
- Summary
- Exam Essentials
1 Laboratorio en vivo in this lesson — see the labs panel →
07 Generative AI Applications with RAG and Agents 5 topics · 1 Laboratorio en vivo +
- Retrieval-Augmented Generation Workflow
- Amazon Bedrock Knowledge Bases
- Amazon Bedrock Agents
- Summary
- Exam Essentials
1 Laboratorio en vivo in this lesson — see the labs panel →
08 Model Customization and Evaluation 8 topics · 1 Laboratorio en vivo +
- 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
1 Laboratorio en vivo in this lesson — see the labs panel →
09 MLOps 7 topics · 1 Laboratorio en vivo +
- MLOps Phases
- MLOps Pipeline
- Automating MLOps
- SageMaker Inference
- Inference Optimizations for Large Language Models
- Summary
- Exam Essentials
1 Laboratorio en vivo in this lesson — see the labs panel →
10 Implementing Responsible AI with AWS Services 7 topics · 1 Laboratorio en vivo +
- Key Principles of Responsible AI
- ML Governance with SageMaker AI
- Amazon SageMaker Clarify
- Amazon Bedrock Guardrails
- Amazon Bedrock Evaluations
- Summary
- Exam Essentials
1 Laboratorio en vivo in this lesson — see the labs panel →
11 AI Security, Governance, and Compliance 5 topics · 2 Laboratorio en vivo +
- Security of AI Systems
- Data Governance Strategies
- Compliance and Regulatory Frameworks in AI
- Summary
- Exam Essentials
2 Laboratorio en vivo in this lesson — see the labs panel →
Laboratorios prácticos Our edge
18 Laboratorio en vivos- Understanding AI and ML Foundations
- Understanding Tokenization in LLM
- Selecting ML Techniques for Different Use Cases
- Creating and Testing an Application on AWS PartyRock
- Creating and Testing a Guardrail
- Exploring and Evaluating Foundation Models Using Amazon Bedrock
- Implementing RAG with Knowledge Bases
- Analyzing a Sample Document Using Amazon Textract
- Translating Language Using Amazon Translate
- Extracting Insights from Text Using Amazon Comprehend
- Building and Evaluating a Classification Model
- Refining Prompts for an Edtech AI Assistant
- Creating and Testing a Basic Customer Support Agent
- Creating a HyperPod Cluster
- Creating and Testing Endpoints in SageMaker
- Evaluating Model Responses Using LLM-as-a-Judge
- Securing AI Systems Against Adversarial Threats
- Monitoring and Detecting Misconfiguration with AWS Config
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
What's the difference between AI, ML, and Deep Learning as covered in this guide?+
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.
Is this guide suitable for beginners with no prior AI experience?+
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.
How much hands-on experience will I get with this study guide?+
This guide includes 18 hands-on labs and 320 practice exercises, designed to solidify your understanding of AWS AI/ML services and practical application. Theory without practice is just talk.
Does this course cover the AIF-C01 exam specifically?+
Absolutely. This study guide is meticulously structured around the AWS Certified AI Practitioner AIF-C01 exam objectives, ensuring comprehensive preparation. We cut the fluff and focus on what you need to pass.
What are the key limitations of AI/ML applications I should be aware of?+
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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Start Your AWS AI Certification Journey Today!
Master AWS AI skills with hands-on labs, practice tests, and real-world training for the AIF-C01 certification exam.
- 1 año de acceso completo
- 18 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito