The AWS Certified AI Practitioner (AIF-C01) exam validates in-demand knowledge of AI, machine learning (ML), and generative AI concepts and use cases. This intermediate-level course prepares you for the AWS Certified AI Practitioner (AIF-C01) exam by providing a comprehensive exploration of the exam topics.

You'll delve into the key areas covered on the exam and understand 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 a 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.

This is a bootcamp to review knowledge and to prepare for the exam. It does not include demos and labs.  Please see the prerequisites below.

Intermediate

This course includes subject overview presentations, exam-style questions, use cases, and group discussions and activities.

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

It is preferred that students complete the learning plan in AWS Skill Builder: "Exam Prep Plan: AWS Certified AI Practitioner (AIF-C01).

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.

The following courses (or similar) are recommended but not required:

• Fundamentals of Machine Learning and Artificial Intelligence (1 hour)
• Exploring Artificial Intelligence Use Cases and Applications (1 hour)
• Responsible Artificial Intelligence Practices (1 hour)
• Developing Machine Learning Solutions (1 hour)
• Developing Generative Artificial Intelligence Solutions (1 hour)
• Essentials of Prompt Engineering (1 hour)
• Optimizing Foundation Models (1 hour)
• Security, Compliance, and Governance for AI Solutions (1 hour)
• Generative AI for Executives (0.25 hour)
• Amazon Q Business Getting Started (0.75 hour)
• Amazon Bedrock Getting Started (1 hour)
• Getting Started with Amazon Comprehend: Custom Classification (1.25 hours)
• Build a Question-Answering Bot Using Generative AI (1.5 hours)

Introduction

Domain 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 

Domain 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

Domain 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 

Domain 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

Domain 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

Course completion