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Core
Deduplication (Exact and Fuzzy)
Duplicates creep into data from retries, joins, and multiple sources, and they corrupt counts, training sets, and aggregates. Exact dedup is a window-function job: ROW_NUMBER over a key, keep rank 1. Fuzzy/near-duplicate dedup (same content, slightly different) needs similarity, embeddings or MinHash/LSH to find near-matches at scale without comparing all pairs. Applied-AI interviews probe it because deduping training data and pipeline outputs is constant, and naive all-pairs comparison does not scale.
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