63 real File Formats 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 Row group?
Junior
A.a Parquet column encoding that replaces repeated values with small dictionary indexes, shrinking storage and speeding equality filters
B.the contiguous storage of one column's data for a single Parquet row group, the unit of column pruning
C.the Avro mechanism that lets readers and writers use different schema versions through writer and reader schema resolution
D.the horizontal partition of rows in a Parquet file within which each column is stored together as a chunk
A.Dictionary encoding — Protocol Buffers, a compact binary serialization format using numbered fields and a schema, common for Kafka messages and service APIs
B.Dictionary encoding — the ORC reader capability of skipping stripes and row groups using built-in min/max indexes and bloom filters when a filter is supplied
C.Dictionary encoding — the contiguous storage of one column's data for a single Parquet row group, the unit of column pruning
D.Dictionary encoding — a columnar encoding that replaces repeated values with small integer codes referencing a per-chunk dictionary
A.Run-length encoding — the ORC reader capability of skipping stripes and row groups using built-in min/max indexes and bloom filters when a filter is supplied
B.Run-length encoding — a compression technique that stores a repeated value once together with the count of its consecutive occurrences
C.Run-length encoding — Protocol Buffers, a compact binary serialization format using numbered fields and a schema, common for Kafka messages and service APIs
D.Run-length encoding — the tradeoff where columnar formats like Parquet excel at analytical scans while row formats like Avro suit record-at-a-time writes
17. Which term means: "a probabilistic index in Parquet and ORC that quickly rules out row groups not containing a sought value with no false negatives"?
A.Bloom filter — the horizontal partition of rows in a Parquet file within which each column is stored together as a chunk
B.Bloom filter — using the per-chunk minimum and maximum statistics in Parquet or ORC footers to skip chunks that cannot match a filter
C.Bloom filter — the ORC reader capability of skipping stripes and row groups using built-in min/max indexes and bloom filters when a filter is supplied
D.Bloom filter — a probabilistic index in Parquet and ORC that quickly rules out row groups not containing a sought value with no false negatives
A.Predicate pushdown via min-max — the horizontal Parquet partition holding column chunks for a block of rows, the unit at which statistics and parallel reads operate
B.Predicate pushdown via min-max — a file format that can be divided at block boundaries so multiple tasks read one file in parallel, as Parquet and ORC allow
C.Predicate pushdown via min-max — using the per-chunk minimum and maximum statistics in Parquet or ORC footers to skip chunks that cannot match a filter
D.Predicate pushdown via min-max — a columnar encoding that replaces repeated values with small integer codes referencing a per-chunk dictionary
A.Footer metadata — Protocol Buffers, a compact binary serialization format using numbered fields and a schema, common for Kafka messages and service APIs
B.Footer metadata — the ORC reader capability of skipping stripes and row groups using built-in min/max indexes and bloom filters when a filter is supplied
C.Footer metadata — the trailing section of a Parquet or ORC file holding the schema, row-group offsets, and statistics needed to read it
D.Footer metadata — the in-memory columnar format enabling zero-copy data exchange between engines and languages without serialization overhead
A.Schema evolution in Avro — the smallest unit of encoding and compression within a Parquet column chunk, also carrying its own statistics
B.Schema evolution in Avro — the Avro mechanism that lets readers and writers use different schema versions through writer and reader schema resolution
C.Schema evolution in Avro — the trailing section of a Parquet or ORC file holding the schema, row-group offsets, and statistics needed to read it
D.Schema evolution in Avro — the in-memory columnar format enabling zero-copy data exchange between engines and languages without serialization overhead
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