Data and Artificial Intelligence Concepts Questions

Core data engineering and applied AI/ML concepts spanning the full data-to-model lifecycle. Covers data modeling, data warehouse versus data lake trade offs, batch versus real time processing, streaming and event driven pipelines, extract transform load (ETL) and extract load transform (ELT) approaches, and analytics and reporting patterns including key performance indicator and metric design. On the machine learning side, covers model training, validation, and inference, feature engineering, model deployment and monitoring, and machine learning operations (MLOps) and governance. Candidates should be able to reason about how these architectural and modeling choices affect latency, cost, and accuracy, and to communicate the resulting technical trade offs and risks clearly to non-technical stakeholders.