AIF-C01

AWS Certified AI Practitioner

Prepare for the AIF-C01 exam and build foundational knowledge of AI and machine learning concepts, generative AI applications, and responsible AI practices on AWS.

75 lessons
12h 30m
5 modules
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Course Content

5 modules · 75 lessons

Module 1: Fundamentals of AI and ML

Understand core AI and ML concepts, practical use cases, and the ML development lifecycle. (20% of exam)

Introduction to AI and Machine Learning

Learn the fundamental concepts and terminology of artificial intelligence and machine learning.

Types of Machine Learning

Understand supervised unsupervised and reinforcement learning approaches.

AI Use Cases and Applications

Explore practical applications of AI across different industries.

ML Development Lifecycle

Understand the end-to-end machine learning development process.

Module 2: Fundamentals of Generative AI

Learn generative AI concepts, foundation models, and AWS infrastructure for GenAI. (24% of exam)

Introduction to Generative AI

Understand the core concepts and terminology of generative AI.

Foundation Models and LLMs

Learn about foundation models and large language models.

Transformers and Attention

Understand transformer architecture and attention mechanisms.

GenAI Model Lifecycle

Understand the lifecycle of generative AI models.

AWS GenAI Infrastructure

Learn about AWS services for generative AI workloads.

Module 3: Security Compliance and Governance

Implement security, compliance, and governance for AI workloads. (14% of exam)

AWS Security for AI

Secure AI workloads on AWS.

Data Protection and Privacy

Protect data used in AI systems.

Compliance Frameworks

Understand compliance requirements for AI.

AI Governance

Implement governance frameworks for AI.

Module 4: Applications of Foundation Models

Design and implement foundation model applications with prompt engineering and customization. (28% of exam)

Foundation Model Design

Design considerations for foundation model applications.

Prompt Engineering Fundamentals

Master the basics of effective prompt engineering.

RAG and Knowledge Bases

Implement retrieval-augmented generation patterns.

Fine-Tuning and Customization

Customize foundation models for specific use cases.

Model Evaluation

Evaluate foundation model performance.

Agents and Tool Use

Build AI agents that can use tools and take actions.

Module 5: Guidelines for Responsible AI

Develop AI systems responsibly with transparency, fairness, and human oversight. (14% of exam)

Responsible AI Principles

Understand the core principles of responsible AI development.

Bias and Fairness

Detect and mitigate bias in AI systems.

Transparency and Explainability

Build transparent and explainable AI systems.

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