GK910029A Exam Prep: AWS Certified AI Pracitioner

COURSE OBJECTIVE:
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.

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

COURSE PREREQUISITES:
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.

COURSE 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 lifecycleModule 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 applicationsModule 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 performanceModule 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 modelsModule 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

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