BigQuery interview questions

1. A.A BigQuery table type that governs and queries data stored in your own object storage, unifying access control across the warehouse and lake.
2. B.A pool of purchased BigQuery slots, defined by a baseline plus an autoscaling ceiling, that workloads are assigned to via assignments.
3. C.A BigQuery table defined over data in Cloud Storage or other sources, queried in place without ingesting into managed storage.
4. D.A BigQuery behavior where unused slots from one reservation are temporarily lent to other reservations needing capacity.

Answer + AI explanation with Pro

1. A.BigLake table
2. B.BigQuery managed Iceberg
3. C.ingestion-time partitioning
4. D.slot

Answer + AI explanation with Pro

1. A.BigLake table — A BigQuery table type storing data as Apache Iceberg in Cloud Storage with BigQuery handling writes, DML, and metadata via BigLake metastore.
2. B.BigLake table — A BigQuery table physically divided by a date, timestamp, integer range, or ingestion time so queries scan only relevant partitions.
3. C.BigLake table — BigQuery partitioning that divides a table by ranges of an integer column defined by a start, end, and interval.
4. D.BigLake table — A BigQuery table type that governs and queries data stored in your own object storage, unifying access control across the warehouse and lake.

Answer + AI explanation with Pro

1. A.The capacity-based pricing tiers (Standard, Enterprise, Enterprise Plus) that provision slots with autoscaling instead of paying per byte scanned.
2. B.A BigQuery table type storing data as Apache Iceberg in Cloud Storage with BigQuery handling writes, DML, and metadata via BigLake metastore.
3. C.An in-memory acceleration layer for BigQuery that caches data to deliver sub-second dashboard queries.
4. D.A BigQuery table defined over data left in place (such as files in Cloud Storage) so it can be queried without loading it into BigQuery storage.

Answer + AI explanation with Pro

1. A.integer-range partitioning
2. B.slot
3. C.ingestion-time partitioning
4. D.BigQuery managed Iceberg

Answer + AI explanation with Pro

1. A.BigQuery managed Iceberg — A BigQuery table type storing data as Apache Iceberg in Cloud Storage with BigQuery handling writes, DML, and metadata via BigLake metastore.
2. B.BigQuery managed Iceberg — BigQuery partitioning that buckets rows by their load time using the pseudo-column _PARTITIONTIME, requiring no explicit partition column.
3. C.BigQuery managed Iceberg — A BigQuery index that accelerates SEARCH() and needle-in-haystack lookups over text and semi-structured columns without scanning the whole table.
4. D.BigQuery managed Iceberg — A BigQuery view granted access to a source dataset so users can query its results without direct permission on the underlying tables.

Answer + AI explanation with Pro

1. A.A BigQuery table whose data is sorted by clustering columns within each partition so the engine prunes blocks and scans fewer bytes.
2. B.A BigQuery table whose data is sorted by clustering columns to reduce bytes scanned and speed up filters and aggregations on those columns.
3. C.A serverless, fully managed runtime metastore implementing the Iceberg REST spec so BigQuery and engines like Spark share one catalog.
4. D.A committed pool of BigQuery slots allocated to projects or workloads to guarantee capacity and control cost under Editions pricing.

Answer + AI explanation with Pro

1. A.BigLake table
2. B.slot
3. C.BigLake metastore
4. D.authorized view

Answer + AI explanation with Pro

1. A.BigLake metastore — A BigQuery behavior where unused slots from one reservation are temporarily lent to other reservations needing capacity.
2. B.BigLake metastore — A serverless, fully managed runtime metastore implementing the Iceberg REST spec so BigQuery and engines like Spark share one catalog.
3. C.BigLake metastore — BigQuery partitioning that divides a table by ranges of an integer column defined by a start, end, and interval.
4. D.BigLake metastore — A BigQuery table type storing data as Apache Iceberg in Cloud Storage with BigQuery handling writes, DML, and metadata via BigLake metastore.

Answer + AI explanation with Pro

1. A.An in-memory acceleration layer for BigQuery that caches data to deliver sub-second dashboard queries.
2. B.A BigQuery table defined over data left in place (such as files in Cloud Storage) so it can be queried without loading it into BigQuery storage.
3. C.A BigQuery table defined over data in Cloud Storage or other sources, queried in place without ingesting into managed storage.
4. D.A BigQuery table whose data is sorted by clustering columns within each partition so the engine prunes blocks and scans fewer bytes.

Answer + AI explanation with Pro

1. A.BI Engine
2. B.BigLake table
3. C.search index
4. D.integer-range partitioning

Answer + AI explanation with Pro

1. A.BI Engine — An in-memory acceleration layer for BigQuery that caches data to deliver sub-second dashboard queries.
2. B.BI Engine — A BigQuery behavior where unused slots from one reservation are temporarily lent to other reservations needing capacity.
3. C.BI Engine — A BigQuery index that accelerates SEARCH() and needle-in-haystack lookups over text and semi-structured columns without scanning the whole table.
4. D.BI Engine — A BigQuery table whose data is sorted by clustering columns to reduce bytes scanned and speed up filters and aggregations on those columns.

Answer + AI explanation with Pro

1. A.A BigQuery table defined over data left in place (such as files in Cloud Storage) so it can be queried without loading it into BigQuery storage.
2. B.A BigQuery table physically divided by a date, timestamp, integer range, or ingestion time so queries scan only relevant partitions.
3. C.The always-on slot capacity of a reservation; autoscaling slots are added on top up to the max reservation size when demand exceeds it.
4. D.A precomputed, automatically and incrementally maintained query result that BigQuery can transparently substitute to speed up matching queries.

Answer + AI explanation with Pro

1. A.partitioned table
2. B.Editions
3. C.BigLake metastore
4. D.materialized view

Answer + AI explanation with Pro

1. A.materialized view — A pool of purchased BigQuery slots, defined by a baseline plus an autoscaling ceiling, that workloads are assigned to via assignments.
2. B.materialized view — A BigQuery unit of virtual CPU and memory representing compute capacity; queries are allocated slots, and reservations guarantee them.
3. C.materialized view — A BigQuery table whose data is sorted by clustering columns within each partition so the engine prunes blocks and scans fewer bytes.
4. D.materialized view — A precomputed, automatically and incrementally maintained query result that BigQuery can transparently substitute to speed up matching queries.

Answer + AI explanation with Pro

1. A.BigQuery's tiered compute pricing model (Standard, Enterprise, Enterprise Plus) using slot-based capacity with autoscaling and commitments.
2. B.A BigQuery index that accelerates SEARCH() and needle-in-haystack lookups over text and semi-structured columns without scanning the whole table.
3. C.The capacity-based pricing tiers (Standard, Enterprise, Enterprise Plus) that provision slots with autoscaling instead of paying per byte scanned.
4. D.A BigQuery capability to create and run machine learning models using SQL CREATE MODEL statements without moving data out.

Answer + AI explanation with Pro

1. A.idle slot sharing
2. B.Editions
3. C.reservation
4. D.BigQuery ML

Answer + AI explanation with Pro

1. A.BigQuery ML — A BigQuery table whose data is sorted by clustering columns to reduce bytes scanned and speed up filters and aggregations on those columns.
2. B.BigQuery ML — BigQuery partitioning that divides a table by ranges of an integer column defined by a start, end, and interval.
3. C.BigQuery ML — A BigQuery capability to create and run machine learning models using SQL CREATE MODEL statements without moving data out.
4. D.BigQuery ML — A serverless, fully managed runtime metastore implementing the Iceberg REST spec so BigQuery and engines like Spark share one catalog.

Answer + AI explanation with Pro

1. A.A precomputed, automatically and incrementally maintained query result that BigQuery can transparently substitute to speed up matching queries.
2. B.A BigQuery table whose data is sorted by clustering columns to reduce bytes scanned and speed up filters and aggregations on those columns.
3. C.BigQuery's tiered compute pricing model (Standard, Enterprise, Enterprise Plus) using slot-based capacity with autoscaling and commitments.
4. D.The capacity-based pricing tiers (Standard, Enterprise, Enterprise Plus) that provision slots with autoscaling instead of paying per byte scanned.

Answer + AI explanation with Pro

1. A.integer-range partitioning
2. B.partitioned table
3. C.Editions
4. D.BigQuery managed Iceberg

Answer + AI explanation with Pro

1. A.Editions — The capacity-based pricing tiers (Standard, Enterprise, Enterprise Plus) that provision slots with autoscaling instead of paying per byte scanned.
2. B.Editions — BigQuery's tiered compute pricing model (Standard, Enterprise, Enterprise Plus) using slot-based capacity with autoscaling and commitments.
3. C.Editions — BigQuery's high-throughput streaming-and-batch ingestion API that supports exactly-once delivery and stream-level transactions.
4. D.Editions — A BigQuery table whose data is sorted by clustering columns within each partition so the engine prunes blocks and scans fewer bytes.

Answer + AI explanation with Pro

1. A.BigQuery's high-throughput streaming-and-batch ingestion API that supports exactly-once delivery and stream-level transactions.
2. B.A BigQuery index that accelerates SEARCH() and needle-in-haystack lookups over text and semi-structured columns without scanning the whole table.
3. C.A BigQuery unit of virtual CPU and memory representing compute capacity; queries are allocated slots, and reservations guarantee them.
4. D.BigQuery partitioning that divides a table by ranges of an integer column defined by a start, end, and interval.

Answer + AI explanation with Pro

1. A.slot
2. B.BigQuery ML
3. C.authorized view
4. D.BigQuery managed Iceberg

Answer + AI explanation with Pro

1. A.slot — A BigQuery behavior where unused slots from one reservation are temporarily lent to other reservations needing capacity.
2. B.slot — An in-memory analysis accelerator that caches BigQuery data to serve sub-second dashboard queries from tools like Looker.
3. C.slot — A BigQuery unit of virtual CPU and memory representing compute capacity; queries are allocated slots, and reservations guarantee them.
4. D.slot — A BigQuery table defined over data in Cloud Storage or other sources, queried in place without ingesting into managed storage.

Answer + AI explanation with Pro

1. A.A BigQuery table defined over data in Cloud Storage or other sources, queried in place without ingesting into managed storage.
2. B.BigQuery's high-throughput streaming-and-batch ingestion API that supports exactly-once delivery and stream-level transactions.
3. C.A BigQuery table whose data is sorted by clustering columns within each partition so the engine prunes blocks and scans fewer bytes.
4. D.A BigQuery index that accelerates SEARCH() and needle-in-haystack lookups over text and semi-structured columns without scanning the whole table.

Answer + AI explanation with Pro

1. A.ingestion-time partitioning
2. B.external table
3. C.BigQuery ML
4. D.Storage Write API

Answer + AI explanation with Pro

1. A.external table — BigQuery partitioning that buckets rows by their load time using the pseudo-column _PARTITIONTIME, requiring no explicit partition column.
2. B.external table — A BigQuery behavior where unused slots from one reservation are temporarily lent to other reservations needing capacity.
3. C.external table — A BigQuery table defined over data in Cloud Storage or other sources, queried in place without ingesting into managed storage.
4. D.external table — A BigQuery unit of virtual CPU and memory representing compute capacity; queries are allocated slots, and reservations guarantee them.

Answer + AI explanation with Pro

1. A.BigQuery's high-throughput streaming-and-batch ingestion API that supports exactly-once delivery and stream-level transactions.
2. B.BigQuery partitioning that divides a table by ranges of an integer column defined by a start, end, and interval.
3. C.A committed pool of BigQuery slots allocated to projects or workloads to guarantee capacity and control cost under Editions pricing.
4. D.A BigQuery table physically divided by a date, timestamp, integer range, or ingestion time so queries scan only relevant partitions.

Answer + AI explanation with Pro

1. A.search index
2. B.partitioned table
3. C.BigQuery editions
4. D.BigLake table

Answer + AI explanation with Pro

1. A.partitioned table — A BigQuery table physically divided by a date, timestamp, integer range, or ingestion time so queries scan only relevant partitions.
2. B.partitioned table — BigQuery partitioning that divides a table by ranges of an integer column defined by a start, end, and interval.
3. C.partitioned table — An in-memory analysis accelerator that caches BigQuery data to serve sub-second dashboard queries from tools like Looker.
4. D.partitioned table — A pool of purchased BigQuery slots, defined by a baseline plus an autoscaling ceiling, that workloads are assigned to via assignments.

Answer + AI explanation with Pro