Exam Prep: AWS Certified AI Pracitioner

This course is intended for individuals who are preparing for the AWS Certified AI Practitioner (AIF-C01) exam.

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.

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

You are not required to take any specific training before taking this course. However, the following:

• Prerequisite knowledge is recommended prior to taking the AWS Certified AI Practitioner (AIF-C01) exam.

Recommended AWS knowledge

• Familiarity with the core AWS services (for example, Amazon EC2, Amazon S3, AWS Lambda, and Amazon SageMaker AI) and AWS core services use cases.
• Suggested to have up to 6 months of exposure to AI and ML technologies on AWS.
• Are familiar with, but do not necessarily build, solutions using AI and ML technologies on AWS.
• Familiarity with the AWS shared responsibility model for security and compliance in the AWS Cloud.
• Familiarity with AWS Identity and Access Management (IAM) for securing and controlling access to AWS resources.
• Familiarity with the AWS global infrastructure, including the concepts of AWS Regions,
  Availability Zones, and edge locations.
• Familiarity with AWS service pricing models.