[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"me":3,"catalog:en:data-engineer\u002Fpe-incremental-cdc-merge-mechanics":4,"config":191},null,{"field_key":5,"field_name":6,"seniority":7,"topic_key":8,"topic_name":9,"spec_key":7,"spec_name":7,"locale":10,"cell_total":11,"field_total":12,"seniorities":13,"topics":17,"specs":94,"samples":105},"data-engineer","Data Engineer","","pe-incremental-cdc-merge-mechanics","Pe Incremental Cdc Merge Mechanics","en",75,1950,[14,15,16],"junior","mid","senior",[18,21,24,27,30,33,36,39,42,45,48,49,52,55,58,61,64,67,70,73,76,79,82,85,88,91],{"key":19,"name":20,"count":11},"data-governance-lineage","Data Governance Lineage",{"key":22,"name":23,"count":11},"data-modeling-warehousing","Data Modeling Warehousing",{"key":25,"name":26,"count":11},"data-partitioning-scaling","Data Partitioning Scaling",{"key":28,"name":29,"count":11},"data-pipeline-design","Data Pipeline Design",{"key":31,"name":32,"count":11},"data-pipeline-orchestration","Data Pipeline Orchestration",{"key":34,"name":35,"count":11},"data-pipeline-reliability","Data Pipeline Reliability",{"key":37,"name":38,"count":11},"data-quality-validation","Data Quality Validation",{"key":40,"name":41,"count":11},"pe-airflow-scheduling-dependencies","Pe Airflow Scheduling Dependencies",{"key":43,"name":44,"count":11},"pe-dbt-models-materializations-tests","Pe Dbt Models Materializations Tests",{"key":46,"name":47,"count":11},"pe-file-formats-table-formats","Pe File Formats Table Formats",{"key":8,"name":9,"count":11},{"key":50,"name":51,"count":11},"pe-spark-api-semantics-udfs","Pe Spark Api Semantics Udfs",{"key":53,"name":54,"count":11},"pe-spark-execution-shuffle-partitions","Pe Spark Execution Shuffle Partitions",{"key":56,"name":57,"count":11},"st-flink-runtime-checkpointing-state","St Flink Runtime Checkpointing State",{"key":59,"name":60,"count":11},"st-flink-time-watermarks-windows","St Flink Time Watermarks Windows",{"key":62,"name":63,"count":11},"st-kafka-broker-storage-replication","St Kafka Broker Storage Replication",{"key":65,"name":66,"count":11},"st-kafka-connect-schema-registry","St Kafka Connect Schema Registry",{"key":68,"name":69,"count":11},"st-kafka-producer-consumer-semantics","St Kafka Producer Consumer Semantics",{"key":71,"name":72,"count":11},"streaming-fundamentals","Streaming Fundamentals",{"key":74,"name":75,"count":11},"st-streaming-joins-tables","St Streaming Joins Tables",{"key":77,"name":78,"count":11},"wh-bigquery-execution-slots-cost","Wh Bigquery Execution Slots Cost",{"key":80,"name":81,"count":11},"wh-bigquery-storage-partitioning-clustering","Wh Bigquery Storage Partitioning Clustering",{"key":83,"name":84,"count":11},"wh-materialized-views-caching","Wh Materialized Views Caching",{"key":86,"name":87,"count":11},"wh-redshift-distribution-sort-vacuum","Wh Redshift Distribution Sort Vacuum",{"key":89,"name":90,"count":11},"wh-snowflake-compute-warehouses","Wh Snowflake Compute Warehouses",{"key":92,"name":93,"count":11},"wh-snowflake-storage-micropartitions","Wh Snowflake Storage Micropartitions",[95,99,102],{"key":96,"name":97,"count":98},"pipelines-etl","Pipelines \u002F ETL",450,{"key":100,"name":101,"count":98},"streaming","Streaming",{"key":103,"name":104,"count":98},"warehousing","Warehousing",[106,124,138,151,164,178],{"id":107,"topic":9,"difficulty":108,"body":109,"options":110,"correct_key":115,"explanation":123},"01a00ae7-b4ee-782a-b17a-1bab98866825",1,"In a Spark SQL `MERGE INTO` statement, what does a `WHEN MATCHED THEN UPDATE` clause do?",[111,114,117,120],{"key":112,"text":113},"a","Inserts a new row for every row present only in the source table",{"key":115,"text":116},"b","Updates a matching target row per the merge condition",{"key":118,"text":119},"c","Deletes every row in the target table regardless of the merge condition",{"key":121,"text":122},"d","Rewrites the entire target table from scratch on every run","`WHEN MATCHED THEN UPDATE` applies only to target rows that satisfy the merge condition against a source row; unmatched source rows are handled by a separate `WHEN NOT MATCHED` clause.",{"id":125,"topic":9,"difficulty":126,"body":127,"options":128,"correct_key":121,"explanation":137},"01a00ae7-b4ef-77e0-8a3e-b1c0f6c3747a",2,"In a Delta Lake `MERGE INTO`, what is the purpose of a `WHEN NOT MATCHED THEN INSERT` clause?",[129,131,133,135],{"key":112,"text":130},"It marks source rows that failed validation for later review",{"key":115,"text":132},"It removes target rows that no longer appear in the source",{"key":118,"text":134},"It updates target rows whose values differ from the source",{"key":121,"text":136},"It inserts a new target row for an unmatched source key","`WHEN NOT MATCHED` fires when a source row's key does not exist in the target, which is exactly the condition for inserting it as a new row — this is the mechanism that turns a merge into an upsert.",{"id":139,"topic":9,"difficulty":108,"body":140,"options":141,"correct_key":112,"explanation":150},"01a00ae7-b4f1-7498-8c96-7c6226ff3ec8","A Debezium change event for a deleted source row looks like this:\n```json\n{\"before\": {\"id\": 42, \"status\": \"active\"}, \"after\": null, \"op\": \"d\", \"ts_ms\": 1699999999000}\n```\nWhat does `\"op\": \"d\"` tell a consumer of this event?",[142,144,146,148],{"key":112,"text":143},"The row was deleted; `before` shows its last state, `after` is null",{"key":115,"text":145},"The source row was duplicated into a new row with the same primary key",{"key":118,"text":147},"The event is part of the initial snapshot load, not a live change",{"key":121,"text":149},"The source database rejected the delete due to a foreign key constraint","Debezium's `op` field marks the operation type; `\"d\"` is a delete. For deletes, `after` is null because the row no longer exists, while `before` still carries the row's state just prior to the delete.",{"id":152,"topic":9,"difficulty":126,"body":153,"options":154,"correct_key":118,"explanation":163},"01a00ae7-b4f2-7f28-b08c-243704968549","Why is 'the source side of a MERGE condition matches more than one row for the same target row' considered the most common `MERGE INTO` failure in practice?",[155,157,159,161],{"key":112,"text":156},"Because most SQL engines silently ignore extra source rows instead of erroring",{"key":115,"text":158},"Because it only occurs when the target table has no primary key defined",{"key":118,"text":160},"Because Delta Lake rejects it when an update\u002Fdelete clause is present",{"key":121,"text":162},"Because it triggers a full table scan on every subsequent query, not an error","Delta Lake and similar engines raise an explicit error (e.g. `UnsupportedOperationException`) when the merge condition maps multiple source rows onto a single target row and a `WHEN MATCHED` update\u002Fdelete clause exists, because it would be ambiguous which source row should win — and duplicate keys in a CDC or extract batch are a routine occurrence.",{"id":165,"topic":9,"difficulty":166,"body":167,"options":168,"correct_key":115,"explanation":177},"01a00ae7-b4f3-7eb6-997d-21d597650359",3,"Before running a `MERGE INTO` where the source might contain more than one row per key, which pre-processing step avoids the multiple-match error?",[169,171,173,175],{"key":112,"text":170},"Sorting the source by key and relying on the sort order for correctness",{"key":115,"text":172},"Deduplicating the source with `ROW_NUMBER()` per key",{"key":118,"text":174},"Adding a `LIMIT 1` clause to the `MERGE INTO` statement itself",{"key":121,"text":176},"Increasing `spark.sql.shuffle.partitions` before the merge runs","Sorting alone doesn't remove duplicate rows, `LIMIT 1` on a `MERGE INTO` statement isn't valid, and shuffle partition count has nothing to do with duplicate keys. Windowing the source by key, ordering by a recency column, and keeping only the top row per key guarantees at most one source row per key before the merge condition is evaluated.",{"id":179,"topic":9,"difficulty":126,"body":180,"options":181,"correct_key":112,"explanation":190},"01a00ae7-b4f4-78ad-891c-bc3854c9f354","What does a Spark `INSERT OVERWRITE TABLE t PARTITION (dt='2026-08-15') SELECT ...` statement do to the target partition?",[182,184,186,188],{"key":112,"text":183},"It replaces the entire partition's contents with the SELECT result",{"key":115,"text":185},"It appends the SELECT result to whatever rows already exist in that partition",{"key":118,"text":187},"It merges the SELECT result with existing rows on a primary key",{"key":121,"text":189},"It only updates columns that changed, leaving unchanged rows untouched","`INSERT OVERWRITE` on a specific partition discards the existing content of that partition and replaces it wholesale with the query result — there is no row-level matching involved, unlike `MERGE 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