# Content Domain 1: Fundamentals of AI and ML
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Domain 1 covers the fundamentals of AI and ML and represents 20% of the scored content on the exam.

**Topics**
+ [Task Statement 1.1: Explain basic AI concepts and terminologies.](#ai-practitioner-01-task1.1)
+ [Task Statement 1.2: Identify practical use cases for AI.](#ai-practitioner-01-task1.2)
+ [Task Statement 1.3: Describe the ML development lifecycle.](#ai-practitioner-01-task1.3)

## Task Statement 1.1: Explain basic AI concepts and terminologies.
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Objectives:
+ 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 models(LLMs)).
+ Describe the similarities and differences between AI, ML, GenAI, and deep learning.
+ Describe various types of inferencing (for example, batch, real-time).
+ Describe the different types of data in AI models (for example, labeled and unlabeled, tabular, time-series, image, text, structured and unstructured).
+ Describe supervised learning, unsupervised learning, and reinforcement learning.

## Task Statement 1.2: Identify practical use cases for AI.
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Objectives:
+ 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 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).
+ Explain the capabilities of AWS managed AI/ML services (for example, Amazon SageMaker AI, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, Amazon Polly).

## Task Statement 1.3: Describe the ML development lifecycle.
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Objectives:
+ Describe components of an ML pipeline (for example, data collection, exploratory data analysis [EDA], data pre-processing, feature engineering, model training, hyperparameter tuning, evaluation, deployment, monitoring).
+ Describe sources of ML 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 ML pipeline (for example, SageMaker AI, SageMaker Data Wrangler, SageMaker Feature Store, SageMaker Model Monitor).
+ 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, Area Under the Curve [AUC], F1 score) and business metrics (for example, cost per user, development costs, customer feedback, return on investment [ROI]) to evaluate ML models.