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merge

AI, ML & GenAI interview questions tagged merge, across every topic.

4 questions · 1 unlocked for you

Concepts behind "merge"

The curriculum that explains the ideas these questions test.

Core
🗄️ Data & SQL EngineeringSign in
Idempotent Data PipelinesData pipelines fail and get rerun, so a pipeline has to be idempotent: running it again yields the same result rather than duplicated or corrupted data. You get there with insert-overwrite by partition, MERGE/upsert keyed on a business id, and deterministic transforms, instead of blind appends that double-count on retry. AI, ML, and GenAI interviews probe it because flaky pipelines are the norm, and a non-idempotent pipeline turns a routine retry into duplicated revenue numbers or a corrupted table.
Core
🗄️ Data & SQL EngineeringSign in
Slowly Changing Dimensions (SCD)Slowly changing dimensions are the patterns for handling dimension attributes that change over time, such as a customer moving cities or a product changing category. Type 1 overwrites history, Type 2 retains versioned rows with effective dates and a current flag, and Type 3 retains a prior-value column. AI, ML, and GenAI interviews probe it because answering what something looked like at the time of an event requires deliberate history tracking, and most analysts only know how to overwrite.
Core
🗄️ Data & SQL EngineeringSign in
Incremental Models and MERGE/UPSERTIncremental models process only new or changed rows rather than rebuilding a table from scratch, using a high-watermark to select the delta and a MERGE/UPSERT to apply it. The hard parts are late-arriving data, idempotent re-runs, and choosing a watermark that does not quietly drop rows. AI, ML, and GenAI interviews probe it because full refreshes do not scale, and a subtly wrong incremental quietly loses or double-counts data.