AWS aws-certification documentation change
Summary
Expanded AI certification content to include generative AI, agentic AI, foundation models, and updated AWS services. Added new objective on model selection criteria.
Security assessment
Changes update educational content about AI/ML concepts without addressing security vulnerabilities. The added regulatory/explainability criteria in model selection relates to compliance best practices, not security fixes or features.
Diff
diff --git a/aws-certification/latest/ai-practitioner-01/ai-practitioner-01-domain1.md b/aws-certification/latest/ai-practitioner-01/ai-practitioner-01-domain1.md index 0b4e791cf..5aa025ba3 100644 --- a//aws-certification/latest/ai-practitioner-01/ai-practitioner-01-domain1.md +++ b//aws-certification/latest/ai-practitioner-01/ai-practitioner-01-domain1.md @@ -7 +7 @@ -Task Statement 1.1: Explain basic AI concepts and terminologies.Task Statement 1.2: Identify practical use cases for AI.Task Statement 1.3: Describe the ML development lifecycle. +Task Statement 1.1: Explain basic AI concepts and terminologies.Task Statement 1.2: Identify practical use cases for AI.Task Statement 1.3: Describe the AI/ML development lifecycle. @@ -19 +19 @@ Domain 1 covers the fundamentals of AI and ML and represents 20% of the scored c - * Task Statement 1.3: Describe the ML development lifecycle. + * Task Statement 1.3: Describe the AI/ML development lifecycle. @@ -28 +28 @@ 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)). + * 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). @@ -30 +30 @@ Objectives: - * Describe the similarities and differences between AI, ML, GenAI, and deep learning. + * Describe the similarities and differences between AI, ML, GenAI, deep learning, and agentic AI. @@ -32 +32 @@ Objectives: - * Describe various types of inferencing (for example, batch, real-time). + * Describe various types of inferencing (for example, batch, real-time, asynchronous, serverless). @@ -36 +36 @@ Objectives: - * Describe supervised learning, unsupervised learning, and reinforcement learning. + * Describe different types of AI/ML learning (for example, supervised learning, unsupervised learning, reinforcement learning methods). @@ -49 +49 @@ Objectives: - * Select the appropriate ML techniques for specific use cases (for example, regression, classification, clustering). + * Select the appropriate AI/ML techniques for specific use cases (for example, regression, classification, clustering). @@ -51 +51 @@ Objectives: - * Identify examples of real-world AI applications (for example, computer vision, NLP, speech recognition, recommendation systems, fraud detection, forecasting). + * Identify examples of real-world AI applications (for example, computer vision, NLP, speech recognition, recommendation systems, fraud detection, forecasting, knowledge bases, agentic AI). @@ -54,0 +55 @@ Objectives: + * 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). @@ -58 +59,2 @@ Objectives: -## Task Statement 1.3: Describe the ML development lifecycle. + +## Task Statement 1.3: Describe the AI/ML development lifecycle. @@ -62 +64 @@ 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 and differentiate components of an AI/ML pipeline. @@ -64 +66 @@ Objectives: - * Describe sources of ML models (for example, open source pre-trained models, training custom models). + * Describe sources of FM models (for example, open source pre-trained models, training custom models). @@ -68 +70 @@ Objectives: - * 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). + * 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). @@ -72 +74 @@ Objectives: - * 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. + * 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.