Course Outline
AWS Certified AI Practitioner
AWS101 | Day | 1 DaysThis class delves into the key areas covered on the exam, understanding how they relate to developing AI and machine learning solutions on the AWS platform. Through detailed explanations and walkthroughs of exam-style questions, you'll reinforce your knowledge, identify gaps in your understanding, and gain valuable strategies for tackling questions effectively.
The course includes review of exam-style sample questions, to help you recognize incorrect responses and hone your test-taking abilities. By the end, you'll have a firm grasp on the concepts and practical applications tested on the AWS Certified AI Practitioner certification exam.
Upcoming Dates:
- Jun 08, 2026 - Jun 08, 2026
- Jul 13, 2026 - Jul 13, 2026
- Aug 14, 2026 - Aug 14, 2026
- Sep 25, 2026 - Sep 25, 2026
- Oct 09, 2026 - Oct 09, 2026
Who should take this course
This course is intended for individuals who are preparing for the AWS Certified AI Practitioner (AIF-C01) exam.
Course Objectives
In this course, you will learn to:
- Identify the scope and content tested by the AWS Certified AI Practitioner (AIF-C01) exam.
- Practice exam style questions and evaluate your preparation strategy.
- Examine use cases and differentiate between them.
Course Outline
Module 1: Fundamentals of AI and ML
1.1: Explain basic AI concepts and terminologies
1.2: Identify practical use cases for AI
1.3: Describe the ML development lifecycle
Module 2: Fundamentals of Generative AI
2.1: Explain the basic concepts of generative AI
2.2: Understand the capabilities and limitations of generative AI for solving business problems
2.3: Describe AWS infrastructure and technologies for building generative AI applications
Module 3: Applications of Foundation Models
3.1: Describe design considerations for applications that use foundation models
3.2: Choose effective prompt engineering techniques
3.3: Describe the training and fine-tuning process for foundation models
3.4: Describe methods to evaluate foundation model performance
Module 4: Guidelines for Responsible AI
4.1: Explain the development of AI systems that are responsible
4.2: Recognize the importance of transparent and explainable models
Module 5: Security, Compliance, and Governance for AI Solutions
5.1: Explain methods to secure AI systems
5.2: Recognize governance and compliance regulations for AI systems
