Core AI, ML, and deep learning concepts, practical use cases, and the ML development lifecycle.
This domain covers 20% of the exam. It tests foundational understanding of AI/ML terminology (AI, ML, deep learning, neural networks, NLP, LLMs), the differences between supervised, unsupervised, and reinforcement learning, practical use cases for AI/ML, AWS managed AI/ML services (SageMaker, Transcribe, Translate, Comprehend, Lex, Polly), the ML development lifecycle from data collection through deployment and monitoring, MLOps concepts, and model performance metrics (accuracy, AUC, F1 score) alongside business metrics (ROI, cost per user).
5 minutes
5 Questions
Domain 1: Fundamentals of AI and ML forms a critical foundation of the AWS Certified AI Practitioner (AIF-C01) exam, typically accounting for about 20% of the total score. This domain tests your understanding of core AI and ML concepts, terminology, and practical applications.
**Key Areas Covered:**
1. **Basic AI/ML Concepts:** You need to understand the differences between Artificial Intelligence, Machine Learning, and Deep Learning. AI is the broadest concept of machines mimicking human intelligence, ML is a subset where systems learn from data, and Deep Learning uses neural networks with multiple layers to process complex patterns.
2. **Types of Machine Learning:** This includes Supervised Learning (using labeled data for classification and regression), Unsupervised Learning (finding patterns in unlabeled data through clustering and dimensionality reduction), Semi-supervised Learning, and Reinforcement Learning (learning through rewards and penalties).
3. **Generative AI Fundamentals:** Understanding foundation models, Large Language Models (LLMs), transformers architecture, diffusion models, and concepts like tokens, context windows, embeddings, prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG).
4. **ML Development Lifecycle:** Knowledge of data collection, data preprocessing, feature engineering, model training, evaluation, deployment, and monitoring. Understanding the iterative nature of ML pipelines is essential.
5. **Model Evaluation Metrics:** Familiarity with accuracy, precision, recall, F1-score, AUC-ROC for classification tasks, and RMSE, MAE for regression problems.
6. **AI/ML Use Cases:** Recognizing practical business applications such as natural language processing, computer vision, recommendation systems, forecasting, fraud detection, and chatbots.
7. **Key Terminology:** Understanding concepts like overfitting, underfitting, bias-variance tradeoff, hyperparameters, inference, training data vs. test data, and neural network basics.
This domain ensures practitioners have a solid theoretical grounding before applying AWS-specific AI/ML services, making it essential for successfully leveraging cloud-based AI solutions.Domain 1: Fundamentals of AI and ML forms a critical foundation of the AWS Certified AI Practitioner (AIF-C01) exam, typically accounting for about 20% of the total score. This domain tests your understanding of core AI and ML concepts, terminology, and practical applications.
**Key Areas Covered:…
