60 real Cost & Performance questions from the Data Engineering bank, as asked in Indian campus drives and tech interviews. Every question has a verified answer and an AI-tutor explanation on placd — free to start.
1. What is partition pruning?
Mid
A.Charges incurred when moving data out of a cloud region or provider, a factor in multi-cloud and cross-region pipeline design.
B.Skipping entire partitions that cannot match a query's filter, the primary way partitioning reduces scanned data and cost.
C.A cache that returns a previously computed query result instantly when the same query runs again over unchanged data, scanning zero bytes.
D.Matching warehouse or slot capacity to workload so jobs finish quickly without paying for idle or oversized compute.
A.partition pruning — Skipping entire partitions that cannot match a query's filter, the primary way partitioning reduces scanned data and cost.
B.partition pruning — Physically co-locating rows by frequently filtered columns so the engine reads fewer micro-partitions or blocks for selective queries.
C.partition pruning — The primary cost driver for on-demand scan-priced warehouses; minimized via partition pruning, clustering, and selecting only needed columns.
D.partition pruning — When a query exceeds available memory and writes intermediate data to local or remote disk, a major performance and cost red flag.
5. Which term means: "Physically co-locating rows by frequently filtered columns so the engine reads fewer micro-partitions or blocks for selective queries."?
A.clustering — An uneven distribution of values across partitions or keys that overloads a few tasks, creating stragglers that dominate job runtime.
B.clustering — Physically co-locating rows by frequently filtered columns so the engine reads fewer micro-partitions or blocks for selective queries.
C.clustering — A join that ships a small table in full to every executor so each partition of the large table joins locally, avoiding a shuffle.
D.clustering — Refreshing only changed partitions of a derived table instead of rebuilding it fully, the main lever for cheap, frequent transformations.
A.auto-suspend — Configuring compute to pause after idle time so you stop paying for unused warehouse runtime, a key cost control.
B.auto-suspend — The primary cost driver for on-demand scan-priced warehouses; minimized via partition pruning, clustering, and selecting only needed columns.
C.auto-suspend — Refreshing only changed partitions of a derived table instead of rebuilding it fully, the main lever for cheap, frequent transformations.
D.auto-suspend — Choosing reserved capacity (lower unit cost, fixed commitment) versus pay-per-use (flexible, higher unit cost) based on workload predictability.
11. Which term means: "When a query exceeds available memory and writes intermediate data to local or remote disk, a major performance and cost red flag."?
A.Refreshing only changed partitions of a derived table instead of rebuilding it fully, the main lever for cheap, frequent transformations.
B.Reading only needed columns instead of all columns, cutting bytes scanned and improving performance in columnar engines.
C.Choosing between recomputing a query each time (cheaper storage, costlier compute) and precomputing it as a table or MV (faster reads, refresh cost).
D.An uneven distribution of values across partitions or keys that overloads a few tasks, creating stragglers that dominate job runtime.
14. Which term means: "Choosing between recomputing a query each time (cheaper storage, costlier compute) and precomputing it as a table or MV (faster reads, refresh cost)."?
A.materialization tradeoff — Reading only needed columns instead of all columns, cutting bytes scanned and improving performance in columnar engines.
B.materialization tradeoff — Choosing between recomputing a query each time (cheaper storage, costlier compute) and precomputing it as a table or MV (faster reads, refresh cost).
C.materialization tradeoff — Skipping entire partitions that cannot match a query's filter, the primary way partitioning reduces scanned data and cost.
D.materialization tradeoff — A performance issue where many tiny files force excessive metadata and task overhead; compacting into larger files restores efficient scans.
17. Which term means: "Tagging and tracking compute spend by team, query, or workload so chargeback and optimization can target the biggest cost drivers."?
A.query cost attribution — A cache that returns a previously computed query result instantly when the same query runs again over unchanged data, scanning zero bytes.
B.query cost attribution — Tagging and tracking compute spend by team, query, or workload so chargeback and optimization can target the biggest cost drivers.
C.query cost attribution — A warehouse setting that idles compute after a period of inactivity so you stop paying for an unused cluster.
D.query cost attribution — Refreshing only changed partitions of a derived table instead of rebuilding it fully, the main lever for cheap, frequent transformations.
A.right-sizing warehouse — Matching warehouse or slot capacity to workload so jobs finish quickly without paying for idle or oversized compute.
B.right-sizing warehouse — Physically co-locating rows by frequently filtered columns so the engine reads fewer micro-partitions or blocks for selective queries.
C.right-sizing warehouse — A join that ships a small table in full to every executor so each partition of the large table joins locally, avoiding a shuffle.
D.right-sizing warehouse — The primary cost driver for on-demand scan-priced warehouses; minimized via partition pruning, clustering, and selecting only needed columns.
23. Which term means: "Charges incurred when moving data out of a cloud region or provider, a factor in multi-cloud and cross-region pipeline design."?
A.data egress cost — Matching warehouse or slot capacity to workload so jobs finish quickly without paying for idle or oversized compute.
B.data egress cost — Charges incurred when moving data out of a cloud region or provider, a factor in multi-cloud and cross-region pipeline design.
C.data egress cost — The primary cost driver for on-demand scan-priced warehouses; minimized via partition pruning, clustering, and selecting only needed columns.
D.data egress cost — When an operation's working set exceeds memory and is written to disk, slowing execution; reduced by tuning partitions or adding memory.
26. Which term means: "The primary cost driver in serverless query engines (BigQuery, Athena), reduced by partitioning, clustering, and selecting fewer columns."?
A.bytes scanned — When a query exceeds available memory and writes intermediate data to local or remote disk, a major performance and cost red flag.
B.bytes scanned — Charges incurred when moving data out of a cloud region or provider, a factor in multi-cloud and cross-region pipeline design.
C.bytes scanned — A cache that returns a previously computed query result instantly when the same query runs again over unchanged data, scanning zero bytes.
D.bytes scanned — The primary cost driver in serverless query engines (BigQuery, Athena), reduced by partitioning, clustering, and selecting fewer columns.
A.SELECT star avoidance — A performance issue where many tiny files force excessive metadata and task overhead; compacting into larger files restores efficient scans.
B.SELECT star avoidance — Reading only needed columns instead of all columns, cutting bytes scanned and improving performance in columnar engines.
C.SELECT star avoidance — Tagging and tracking compute spend by team, query, or workload so chargeback and optimization can target the biggest cost drivers.
D.SELECT star avoidance — Choosing between recomputing a query each time (cheaper storage, costlier compute) and precomputing it as a table or MV (faster reads, refresh cost).
Showing 30 of 60 Cost & Performance questions — the full set, with answers, explanations and an AI tutor on every question, is inside.
Free to start
Answers, AI explanations, and a scored voice mock interview
Sign up free to check your answers with explanations, ask the AI tutor anything on any question, and take one full AI mock interview — scored like a real panel.