AWS Machine Learning Study Roadmap (Retired Exam)

Domain 1: Data Repositories (S3 data lakes, Redshift, RDS, DynamoDB for ML pipelines)

Domain 1: Data Ingestion (Kinesis Data Streams/Firehose, Glue ETL, Data Pipeline, batch vs streaming)

Domain 1: Data Transformation (Glue DataBrew, EMR Spark, Athena, feature engineering techniques)

Domain 2: Data Sanitization (Missing values, outliers, normalization, encoding categorical variables)

Domain 2: Feature Engineering. Feature selection, dimensionality reduction (PCA), data visualization

Domain 2: Data Analysis (Statistical analysis, correlation, distribution analysis, SageMaker Data Wrangler)

Domain 3: ML Frameworks (SageMaker built-in algorithms, XGBoost, Linear Learner, image classification)

Domain 3: Model Training (Hyperparameter tuning, training jobs, distributed training, Spot instances)

Domain 3: Evaluation (Confusion matrix, precision/recall, F1 score, AUC-ROC, overfitting/underfitting)

Domain 4: SageMaker Endpoints (Real-time inference, batch transform, multi-model endpoints, auto-scaling)

Domain 4: MLOps (SageMaker Pipelines, Model Registry, Model Monitor, A/B testing, CI/CD for ML)

Domain 4: Security & Cost (VPC configs, KMS encryption, IAM roles, Spot training, instance selection)

Full Review: Practice exams, SageMaker hands-on labs, Algorithm review, Exam logistics

Final Review: Weak areas deep-dive, Full-length practice tests, Time management strategies