96 real Iceberg questions from the Big Data 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 Hidden partitioning?
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A.an Iceberg spec v3 feature assigning each row a stable _row_id and sequence number to support change tracking
B.an Iceberg feature where partition values are derived from columns via transforms, so queries need not filter on a separate partition column
C.an Iceberg merge-on-read delete file marking removed rows by their file path and row position, efficient to apply but tied to specific data files
D.a named reference to a specific Iceberg snapshot, often used to mark a release or retain a snapshot for a retention period
2. Which term means: "an Iceberg feature where partition values are derived from columns via transforms, so queries need not filter on a separate partition column"?
A.Hidden partitioning — an Iceberg virtual table (such as files, snapshots, history, or partitions) queried to inspect a table's structure and evolution
B.Hidden partitioning — an Iceberg feature where partition values are derived from columns via transforms, so queries need not filter on a separate partition column
C.Hidden partitioning — the Iceberg catalog that adds Git-like branches, tags, and commits over tables so multi-table changes can be versioned and merged
D.Hidden partitioning — an Iceberg merge-on-read delete file marking removed rows by their file path and row position, efficient to apply but tied to specific data files
A.Partition evolution — the Iceberg row-level mode that records changes as delete and data files merged at query time, favoring fast writes over fast reads
B.Partition evolution — the Iceberg ability to change a table's partition scheme over time without rewriting existing data files
C.Partition evolution — an Iceberg update mode that rewrites whole data files on each change, giving fast reads but slower writes
D.Partition evolution — an Iceberg spec v3 binary structure that marks deleted rows compactly per data file, replacing positional delete files
A.Snapshot — the Iceberg SQL statement that performs upserts by matching a source against a target and inserting, updating, or deleting in one atomic operation
B.Snapshot — an immutable, point-in-time version of an Iceberg table listing all data files, enabling time travel and rollback
C.Snapshot — the Iceberg property (none, hash, or range) controlling how Spark redistributes rows across tasks before writing to limit small files and skew
D.Snapshot — an Iceberg update mode that records deletes and updates separately and merges them at query time for faster writes
A.Manifest file — an Iceberg table property declaring how rows should be ordered within data files so writers cluster data and readers prune more effectively
B.Manifest file — an Iceberg merge-on-read delete file marking removed rows by their file path and row position, efficient to apply but tied to specific data files
C.Manifest file — an Iceberg metadata file that lists data files with their partition values and column statistics for pruning
D.Manifest file — an Iceberg update mode that records deletes and updates separately and merges them at query time for faster writes
A.Branch — an Iceberg merge-on-read delete file marking removed rows by column values such as id equals five, flexible to write but costly to apply on read
B.Branch — an Iceberg feature where partition values are derived from columns via transforms, so queries need not filter on a separate partition column
C.Branch — a named, independent line of Iceberg snapshots that supports isolated writes such as a write-audit-publish staging area
D.Branch — an Iceberg spec v3 binary structure that marks deleted rows compactly per data file, replacing positional delete files
A.Tag — the Iceberg SQL statement that performs upserts by matching a source against a target and inserting, updating, or deleting in one atomic operation
B.Tag — an Iceberg feature where partition values are derived from columns via transforms, so queries need not filter on a separate partition column
C.Tag — a named reference to a specific Iceberg snapshot, often used to mark a release or retain a snapshot for a retention period
D.Tag — the Iceberg catalog that adds Git-like branches, tags, and commits over tables so multi-table changes can be versioned and merged
A.Copy-on-write — an Iceberg update mode that rewrites whole data files on each change, giving fast reads but slower writes
B.Copy-on-write — an immutable, point-in-time version of an Iceberg table listing all data files, enabling time travel and rollback
C.Copy-on-write — an Iceberg virtual table (such as files, snapshots, history, or partitions) queried to inspect a table's structure and evolution
D.Copy-on-write — an Iceberg merge-on-read delete file marking removed rows by column values such as id equals five, flexible to write but costly to apply on read
A.Deletion vector — the Iceberg rewrite_data_files action that merges many small files into larger ones and can re-sort data to restore read performance
B.Deletion vector — the Iceberg catalog implementation that stores table metadata pointers in the AWS Glue Data Catalog, the most adopted Iceberg catalog as of 2025
C.Deletion vector — the open-source REST Iceberg catalog originally open sourced by Snowflake, adding role-based access control and multi-engine governance
D.Deletion vector — an Iceberg spec v3 binary structure that marks deleted rows compactly per data file, replacing positional delete files
A.Row lineage — the open-source REST Iceberg catalog originally open sourced by Snowflake, adding role-based access control and multi-engine governance
B.Row lineage — an Iceberg spec v3 feature assigning each row a stable _row_id and sequence number to support change tracking
C.Row lineage — the Iceberg catalog that adds Git-like branches, tags, and commits over tables so multi-table changes can be versioned and merged
D.Row lineage — an Iceberg merge-on-read delete file marking removed rows by column values such as id equals five, flexible to write but costly to apply on read
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