merge
Applied AI 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
Idempotent Data PipelinesData pipelines fail and get rerun, so a pipeline must be idempotent: rerunning it produces the same result, not duplicated or corrupted data. You achieve it with insert-overwrite by partition, MERGE/upsert keyed on a business id, and deterministic transforms, rather than blind appends that double-count on retry. Applied-AI 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.🗄️ Data & SQL EngineeringSign in
Core
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 keeps versioned rows with effective dates and a current flag, and Type 3 keeps a prior-value column. Applied-AI 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.🗄️ Data & SQL EngineeringSign in
Core
Incremental Models and MERGE/UPSERTIncremental models process only new or changed rows instead of 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 silently drop rows. Applied-AI interviews probe it because full refreshes do not scale, and a subtly wrong incremental quietly loses or double-counts data.🗄️ Data & SQL EngineeringSign in