Sampling Techniques: Stratified, Reservoir, Importance

Sampling techniques decide which subset of data you train on, evaluate on, or stream through, and that choice quietly determines whether your numbers match reality. The core methods are uniform, stratified, reservoir for unbounded streams, and importance sampling for rare or reweighted events. AI, ML, and GenAI engineer interviews probe this because a biased sample yields a confidently wrong model and an eval set that lies about production performance.