Exam Prep: AWS Certified AI Pracitioner [GK910029A]

OVERVIEW

AWS Certified AI Practitioner (AIF-C01) is a one-day ILT where you learn how to assess your preparedness for the AWS Certified AI Practitioner (AIF-C01) exam. 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, 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.

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.

AUDIENCE

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

CONTENT

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