AWS Certified AI Practitioner (AIF-C01): Fundamentals of Generative AI
Field | Description / Template |
|---|---|
Purpose | This course introduces the core concepts of Generative AI, including large language models (LLMs), embeddings, prompt engineering, and foundation model lifecycles. It helps learners understand how generative models work, their real-world applications, limitations, and how to leverage AWS generative AI services effectively. The course also prepares learners for the AWS Certified AI Practitioner (AIF-C01) exam with focused reviews and exam tips. |
Audience | Beginners, business professionals, students, developers, and AWS certification aspirants interested in Generative AI and its practical applications. |
Role | AI Practitioner, Prompt Engineer, Business Analyst, Product Manager, Developer, Technical Consultant. |
Domain | Generative AI, AI/ML, Cloud Computing |
Skill Level | Beginner |
Style | Conceptual and use-case-driven learning with real-world examples, simplified explanations of complex AI concepts, AWS service overviews, and exam-focused preparation. |
Duration | 6–10 hours |
Related Technologies | AWS Bedrock, Amazon SageMaker, Foundation Models (LLMs), Embeddings, Vector Databases, Prompt Engineering |
Course Description
This course provides a comprehensive introduction to Generative AI concepts and their implementation within the AWS ecosystem. Learners will explore key ideas such as tokens, embeddings, transformers, and large language models, along with the lifecycle of foundation models.
The course also dives into real-world use cases of Generative AI, highlighting its advantages, limitations, and business value. Learners will understand how to select appropriate models based on requirements and evaluate trade-offs, including cost and performance.
Additionally, the course introduces AWS generative AI services, helping learners understand how AWS enables scalable and secure AI solutions. With exam-focused reviews and practical insights, this course prepares learners for the Generative AI section of the AWS Certified AI Practitioner (AIF-C01) exam.
Who is this course for
Beginners curious about Generative AI and LLMs
Business professionals exploring AI-driven innovation
Developers and analysts working with AI-powered applications
AWS certification aspirants (AI Practitioner)
Product managers and decision-makers evaluating AI use cases
Course Objectives
By the end of this course, learners will be able to:
Understand core Generative AI concepts such as tokens, embeddings, and transformers
Explain how large language models (LLMs) work
Apply prompt engineering techniques effectively
Identify real-world use cases and business applications of Generative AI
Evaluate advantages, limitations, and risks of generative models
Select appropriate models based on use case and constraints
Understand AWS generative AI services and their cost trade-offs
Prepare effectively for the AWS Certified AI Practitioner (AIF-C01) exam
Prerequisites
Basic understanding of cloud computing concepts
No prior experience in AI/ML required
Familiarity with general technology concepts
Curiosity about AI and emerging technologies
Course outline
Section 1: Generative AI Concepts
Tokens and Chunking
Embeddings and Vectors
Transformer-based LLMs
Different Generative AI Models
Prompt Engineering
Foundation Model Lifecycle
Generative AI Concepts Review
Section 2: Use Cases for Generative AI
Generative AI Use Cases
Advantages of Generative AI
Limitations and Challenges
Model Selection Factors
Business Metrics and Value
Generative AI Use Cases Exam Tips
Section 3: Generative AI in AWS
Generative AI in AWS
Advantages of AWS Generative AI Services
Cost Tradeoffs of AWS Generative AI Services
AWS Generative AI Services Review
Section 4: Exam Question Review
Generative AI Concepts
Disadvantages of Generative AI
AWS Generative AI Services
Cost Tradeoffs in AWS Generative AI Services