Barnosky, Timothy
AWS Certified AI Practitioner (AIF-C01) Exam Guide Introduction
The AWS Certified AI Practitioner (AIF-C01) exam is intended for individuals who can effectively demonstrate overall knowledge of AI/ML, generative AI technologies, and associated AWS services and tools, independent of a specific job role.
The exam also validates a candidate’s ability to complete the following tasks:
• Understand AI, ML, and generative AI concepts, methods, and strategies in general and on AWS.
• Understand the appropriate use of AI/ML and generative AI technologies to ask relevant questions within the candidate’s organization.
• Determine the correct types of AI/ML technologies to apply to specific use cases.
• Use AI, ML, and generative AI technologies responsibly.
Target candidate description
The target candidate should have up to 6 months of exposure to AI/ML technologies on AWS. The target candidate uses but does not necessarily build AI/ML solutions on AWS.
Recommended AWS knowledge
The target candidate should have the following AWS knowledge:
• Familiarity with the core AWS services (for example, Amazon EC2, Amazon S3, AWS Lambda, and Amazon SageMaker) and AWS core services use cases
• 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 Version 1.4 AIF-C01
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Job tasks that are out of scope for the target candidate The following list contains job tasks that the target candidate is not expected to be able to perform. This list is non-exhaustive. These tasks are out of scope for the exam:
• Developing or coding AI/ML models or algorithms
• Implementing data engineering or feature engineering techniques
• Performing hyperparameter tuning or model optimization
• Building and deploying AI/ML pipelines or infrastructure
• Conducting mathematical or statistical analysis of AI/ML models
• Implementing security or compliance protocols for AI/ML systems