Content Domain 1: Fundamentals of AI and ML

Define basic AI terms (for example, AI, ML, deep learning, neural networks, computer vision, natural language processing [NLP], model, algorithm, training and inferencing, bias, fairness, fit, large language model [LLM], generative AI [GenAI], agentic AI).

Describe the similarities and differences between AI, ML, GenAI, deep learning, and agentic AI.

Describe various types of inferencing (for example, batch, real-time, asynchronous, serverless).

Describe the different types of data in AI models (for example, labeled and unlabeled, tabular, time-series, image, text, structured and unstructured).

Describe different types of AI/ML learning (for example, supervised learning, unsupervised learning, reinforcement learning methods).

Recognize applications where AI/ML can provide value (for example, assist human decision making, solution scalability, automation).

Determine when AI/ML solutions are not appropriate (for example, cost-benefit analyses, situations when a specific outcome is needed instead of a prediction).

Select the appropriate AI/ML techniques for specific use cases (for example, regression, classification, clustering).

Identify examples of real-world AI applications (for example, computer vision, NLP, speech recognition, recommendation systems, fraud detection, forecasting, knowledge bases, agentic AI).

Explain the capabilities of AWS managed AI/ML services (for example, Amazon SageMaker AI, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, Amazon Polly).

Identify when traditional ML models or foundation models (FMs) are appropriate for a specific use case (for example, based on regulatory concerns, explainability requirements, operational constraints).

Describe and differentiate components of an AI/ML pipeline.

Describe sources of FM models (for example, open source pre-trained models, training custom models).

Describe methods to use a model in production (for example, managed API service, self-hosted API).

Identify relevant AWS services and features for each stage of an AI/ML pipeline (for example, Amazon Bedrock, Amazon Q, Amazon Quick, Kiro, SageMaker AI).

Describe fundamental concepts of ML operations (MLOps) (for example, experimentation, repeatable processes, scalable systems, managing technical debt, achieving production readiness, model monitoring, model re-training).

Describe model performance metrics (for example, accuracy, precision, recall, F1 score) and business metrics (for example, cost per user, development costs, customer feedback, return on investment [ROI]) to evaluate ML models.