Welcome to my repository dedicated to the AWS Certified AI Practitioner (AIF-C01) exam! This repository aims to provide all the necessary information and resources to help you prepare effectively for the exam.
The AWS Certified AI Practitioner exam assesses candidates’ knowledge of artificial intelligence (AI), machine learning (ML), generative AI technologies, and relevant AWS services. It is intended for individuals who can effectively demonstrate a general understanding of AI/ML concepts and their application in AWS.
- Experience: Up to 6 months of exposure to AI/ML technologies on AWS.
- Roles: Uses but does not necessarily build AI/ML solutions on AWS.
- Format: 50 scored questions and 15 unscored questions.
- Scoring: -- Minimum passing score: 700 (out of 1000). -- Unanswered questions are scored as incorrect. -- Exam results are reported as a scaled score.
The exam has the following content domains and associated topics:
- Fundamentals of AI and ML (20%)
- Basic AI concepts and terminologies (e.g., AI, ML, deep learning, neural networks, computer vision, natural language processing [NLP]).
- Similarities and differences between AI, ML, and deep learning.
- Types of inferencing (batch vs. real-time).
- Different types of data in AI models (labeled vs. unlabeled, tabular, time-series, image, text).
- Supervised, unsupervised, and reinforcement learning.
- Fundamentals of Generative AI (24%)
- Understanding generative AI and its applications.
- Use cases of foundation models.
- Impacts and considerations in deploying generative AI solutions.
- Applications of Foundation Models (28%)
- Identify and explain applications of foundation models.
- Integrating foundation models with AWS services.
- Use cases in various industries.
- Guidelines for Responsible AI (14%)
- Principles of responsible AI development and deployment.
- AI ethics, bias, and fairness considerations.
- Importance of model transparency and explainability.
- Security, Compliance, and Governance for AI Solutions (14%)
- Security methods for AI systems (e.g., IAM roles and policies).
- Privacy and compliance concerns in AI applications.
- Best practices for secure data engineering.
To prepare for the exam, it is beneficial to have familiarity with the following AWS concepts:
- Core AWS services (e.g., Amazon EC2, Amazon S3, AWS Lambda, Amazon SageMaker).
- AWS shared responsibility model for security and compliance.
- AWS Identity and Access Management (IAM).
- AWS global infrastructure, including Regions and Availability Zones.
- AWS service pricing models.
- Understand AI, ML, and Generative AI Concepts: Familiarize yourself with basic definitions and concepts related to AI, machine learning, and generative AI.
- Study the ML Development Lifecycle: Learn about the stages of an ML pipeline, including data collection, model training, evaluation, and deployment.
- Explore AWS Managed AI/ML Services: Understand the capabilities and use cases of AWS services such as SageMaker, Amazon Transcribe, and Amazon Lex.
- AWS Certification Website: AWS Certification
- AWS Documentation: AWS Documentation
If you have any tips, resources, or experiences to share, feel free to contribute or leave a comment!