[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"me":3,"catalog:en:data-engineer\u002Fwh-redshift-distribution-sort-vacuum":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","","wh-redshift-distribution-sort-vacuum","Wh Redshift Distribution Sort Vacuum","en",75,1950,[14,15,16],"junior","mid","senior",[18,21,24,27,30,33,36,39,42,45,48,51,54,57,60,63,66,69,72,75,78,81,84,87,88,91],{"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":49,"name":50,"count":11},"pe-incremental-cdc-merge-mechanics","Pe Incremental Cdc Merge Mechanics",{"key":52,"name":53,"count":11},"pe-spark-api-semantics-udfs","Pe Spark Api Semantics Udfs",{"key":55,"name":56,"count":11},"pe-spark-execution-shuffle-partitions","Pe Spark Execution Shuffle Partitions",{"key":58,"name":59,"count":11},"st-flink-runtime-checkpointing-state","St Flink Runtime Checkpointing State",{"key":61,"name":62,"count":11},"st-flink-time-watermarks-windows","St Flink Time Watermarks Windows",{"key":64,"name":65,"count":11},"st-kafka-broker-storage-replication","St Kafka Broker Storage Replication",{"key":67,"name":68,"count":11},"st-kafka-connect-schema-registry","St Kafka Connect Schema Registry",{"key":70,"name":71,"count":11},"st-kafka-producer-consumer-semantics","St Kafka Producer Consumer Semantics",{"key":73,"name":74,"count":11},"streaming-fundamentals","Streaming Fundamentals",{"key":76,"name":77,"count":11},"st-streaming-joins-tables","St Streaming Joins Tables",{"key":79,"name":80,"count":11},"wh-bigquery-execution-slots-cost","Wh Bigquery Execution Slots Cost",{"key":82,"name":83,"count":11},"wh-bigquery-storage-partitioning-clustering","Wh Bigquery Storage Partitioning Clustering",{"key":85,"name":86,"count":11},"wh-materialized-views-caching","Wh Materialized Views Caching",{"key":8,"name":9,"count":11},{"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",[106,124,137,151,164,177],{"id":107,"topic":9,"difficulty":108,"body":109,"options":110,"correct_key":118,"explanation":123},"01a00fd5-7d32-74b4-a8f2-264f029ce92e",1,"In an Amazon Redshift provisioned cluster, which statement correctly describes the leader node's role?",[111,114,117,120],{"key":112,"text":113},"a","It stores a full replica of every table so queries never touch compute nodes.",{"key":115,"text":116},"b","It executes the bulk of the join and aggregation work before sending results to compute nodes.",{"key":118,"text":119},"c","It parses incoming queries, builds execution plans, and coordinates the compute nodes.",{"key":121,"text":122},"d","It is only present in single-node clusters and disappears once you add compute nodes.","The leader node receives queries from clients, parses them, builds the execution plan, and coordinates parallel execution across the compute nodes, then aggregates the results. It doesn't hold a full replica of every table, doesn't do the bulk of join\u002Faggregation work itself, and it exists on multi-node clusters too (separate from compute nodes there).",{"id":125,"topic":9,"difficulty":108,"body":126,"options":127,"correct_key":115,"explanation":136},"01a00fd5-7d33-767a-b800-6b95bd2b220f","In Redshift, what is a 'slice'?",[128,130,132,134],{"key":112,"text":129},"A snapshot of a table taken automatically before every VACUUM.",{"key":115,"text":131},"A CPU\u002Fmemory\u002Fdisk share on a compute node that processes part of its data.",{"key":118,"text":133},"A billing unit used only for Redshift Serverless workgroups.",{"key":121,"text":135},"A separate compute node reserved exclusively for the leader node's metadata.","Each compute node is partitioned into slices, and each slice gets its own share of the node's CPU, memory, and disk to process a portion of the node's data in parallel with the other slices. It has nothing to do with snapshots, Serverless-only billing, or leader-node metadata storage.",{"id":138,"topic":9,"difficulty":139,"body":140,"options":141,"correct_key":112,"explanation":150},"01a00fd5-7d35-708d-a59a-00c6331f0409",2,"Why does the number of slices per compute node matter for how data spreads across a Redshift cluster?",[142,144,146,148],{"key":112,"text":143},"Redshift distributes a table's rows across every slice in the cluster.",{"key":115,"text":145},"Slice count only affects backup file size and has no role in query execution.",{"key":118,"text":147},"Redshift always uses exactly one slice per cluster regardless of node type.",{"key":121,"text":149},"Slices determine which AWS Region a cluster is created in.","A table's rows are distributed across every slice in the cluster according to the table's distribution style, so how many slices exist (node count times slices per node) directly shapes how finely that data — and the parallel work on it — is spread. Slice count isn't about backup size, isn't fixed at one per cluster, and has no bearing on region selection.",{"id":152,"topic":9,"difficulty":139,"body":153,"options":154,"correct_key":118,"explanation":163},"01a00fd5-7d35-7cd7-81f2-b7b792e2e9ca","What does DISTSTYLE EVEN do when you create a table?",[155,157,159,161],{"key":112,"text":156},"Every row is copied to every node so every slice holds the full table.",{"key":115,"text":158},"Rows are grouped onto slices strictly by the values of the primary key.",{"key":118,"text":160},"The leader node hands out rows to slices round-robin, ignoring column values.",{"key":121,"text":162},"Redshift picks between KEY and ALL automatically as the table grows.","With EVEN distribution, the leader node hands out rows to slices round-robin, independent of any column's values. Copying every row to every node describes ALL; grouping by a key column describes KEY; automatically switching between styles as a table grows describes AUTO, not EVEN.",{"id":165,"topic":9,"difficulty":139,"body":166,"options":167,"correct_key":112,"explanation":176},"01a00fd5-7d37-7a9e-804d-71faba96348a","What does DISTSTYLE KEY do?",[168,170,172,174],{"key":112,"text":169},"Rows are distributed to slices based on one designated column's values.",{"key":115,"text":171},"Rows are distributed round-robin across slices regardless of column values.",{"key":118,"text":173},"The full table is copied to every compute node in the cluster.",{"key":121,"text":175},"Redshift chooses the distribution column automatically at query time, per query.","KEY distribution places rows on slices according to the hashed value of one chosen column, so rows sharing the same value in that column end up on the same slice. Round-robin placement describes EVEN, full replication describes ALL, and the distribution column is fixed at table design time, not chosen per query.",{"id":178,"topic":9,"difficulty":179,"body":180,"options":181,"correct_key":121,"explanation":190},"01a00fd5-7d39-77f5-9bf7-20f7971eb33b",3,"What is the direct cost of choosing DISTSTYLE ALL for a table?",[182,184,186,188],{"key":112,"text":183},"Query results become eventually consistent instead of strongly consistent.",{"key":115,"text":185},"The table can no longer be joined with DISTSTYLE KEY tables.",{"key":118,"text":187},"Redshift disables compression encoding for that table.",{"key":121,"text":189},"Storage multiplies by the node count, and loads\u002Fupdates take longer.","ALL distribution puts a full copy of the table on every node, so storage grows roughly in proportion to node count, and loading, updating, or inserting into the table takes longer because every node's copy must be kept in sync. It doesn't touch consistency semantics, joinability with KEY-distributed tables, or compression 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