AWS Certified AI Practitioner (AIF-C01)

Domain 1: Fundamentals of AI and ML (20% of scored content)

Task Statement 1.1: Explain basic AI concepts and terminologies.

• 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]).
• Describe the similarities and differences between AI, ML, 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.

• 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, costbenefit 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, SageMaker, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, Amazon Polly).

Task Statement 1.3: Describe the ML development lifecycle.

• 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).
• Understand 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, Amazon SageMaker Data Wrangler, Amazon SageMaker Feature Store, Amazon SageMaker Model Monitor).