Data Preparation

Collect, transform, and trust the data — 28% of the exam

• Ingesting and Storing Training Data — Formats that make or break training, batch extraction from S3 and databases, streaming ingestion with Kinesis and Flink, SageMaker Feature Store, and where vectors live for AI apps. (5 lessons, 60 min)
• Transforming Data and Engineering Features — Picking the right transform tool — Glue, DataBrew, EMR, Data Wrangler — classic feature engineering, streaming transforms, and preparing text for GenAI: tokens, embeddings, chunking. (5 lessons, 60 min)
• Data Quality, Labeling, and Bias — Quality gates with Glue Data Quality and DataBrew, labeling pipelines, train/validation/test splits and the leakage trap, class imbalance, bias mitigation, and GenAI data integrity. (5 lessons, 60 min)
• Data Architecture for ML: Lakes, Warehouses, Catalogs — The architecture questions behind Domain 1: lake versus warehouse, Glue Data Catalog and Lake Formation, partitioning and storage-cost strategy, and moving data in at scale. (4 lessons, 50 min)