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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COURSES COVERING THIS TOPIC

No lesson covers this one directly yet. These teach the surrounding topic from the beginning.

Applied AI Engineering·The interviewPremium12mTurning this course into a study planA concrete four-week plan mapping the seven modules onto the question bank, plus what to do differently if your interview is next week rather than next month.

RELATED CONCEPTS

Window Functions Data Quality and Contracts Idempotent Data Pipelines: Reruns Without Duplicate Rows Embeddings

PRACTICE THIS IN REAL QUESTIONS

SQL & Data EngineeringHow do you find and remove duplicate rows in SQL, including 'fuzzy' near-duplicates?→SQL & Data EngineeringTurn a raw web crawl into a clean trillion-token LLM training corpus. Design the pipeline.→SQL & Data EngineeringSCD Type 2 in Delta Lake: track history with one atomic MERGE.→SQL & Data EngineeringGroup a stream of user events into sessions in SQL (30-minute inactivity gap) using window functions.→SQL & Data EngineeringTop-N per group in SQL: ROW_NUMBER vs RANK vs DENSE_RANK.→SQL & Data EngineeringDeduplicate events exactly-once over a sliding 7-day window in a high-throughput stream without running out of memory.→

COMPANIES THAT ASSUME THIS

Scale AI Palantir Databricks Snowflake Zendesk Clay

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