36 real File Formats 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 columnar storage?
Mid
A.Pushing filter conditions down to the storage layer so only matching row groups or files are read, using column statistics to skip data.
B.Storing values of each column together so analytic scans read only needed columns and compress better, the basis of Parquet and ORC.
C.An optimized row-columnar file format from the Hadoop ecosystem with strong compression, lightweight indexes, and built-in statistics.
D.Hierarchical structures (structs, arrays, maps) that columnar formats like Parquet encode with repetition and definition levels.
2. Which term means: "Storing values of each column together so analytic scans read only needed columns and compress better, the basis of Parquet and ORC."?
A.columnar storage — Pushing filter conditions down to the storage layer so only matching row groups or files are read, using column statistics to skip data.
B.columnar storage — A row-oriented binary format with a self-describing schema, well suited to streaming and write-heavy workloads and schema evolution.
C.columnar storage — An optimized row-columnar file format from the Hadoop ecosystem with strong compression, lightweight indexes, and built-in statistics.
D.columnar storage — Storing values of each column together so analytic scans read only needed columns and compress better, the basis of Parquet and ORC.
5. Which term means: "An optimized row-columnar file format from the Hadoop ecosystem with strong compression, lightweight indexes, and built-in statistics."?
A.ORC — A horizontal chunk of a Parquet file holding column chunks plus min/max statistics, the unit at which readers prune and parallelize.
B.ORC — A compression technique replacing repeated values with small integer codes referencing a dictionary, very effective for low-cardinality columns.
C.ORC — Performance degradation from many tiny files that bloat metadata and scheduling overhead, mitigated by compaction into larger files.
D.ORC — An optimized row-columnar file format from the Hadoop ecosystem with strong compression, lightweight indexes, and built-in statistics.
8. Which term means: "Pushing filter conditions down to the storage layer so only matching row groups or files are read, using column statistics to skip data."?
A.predicate pushdown — A maintenance operation that rewrites many small data files into fewer large ones to improve scan efficiency in lakes and open table formats.
B.predicate pushdown — Pushing filter conditions down to the storage layer so only matching row groups or files are read, using column statistics to skip data.
C.predicate pushdown — A horizontal chunk of a Parquet file holding column chunks plus min/max statistics, the unit at which readers prune and parallelize.
D.predicate pushdown — A row-oriented binary format with a self-describing schema, well suited to streaming and write-heavy workloads and schema evolution.
11. Which term means: "A horizontal chunk of a Parquet file holding column chunks plus min/max statistics, the unit at which readers prune and parallelize."?
14. Which term means: "A compression technique replacing repeated values with small integer codes referencing a dictionary, very effective for low-cardinality columns."?
A.dictionary encoding — The smallest unit of storage and compression within a Parquet column chunk, holding encoded values, definition, and repetition levels.
B.dictionary encoding — A compression technique replacing repeated values with small integer codes referencing a dictionary, very effective for low-cardinality columns.
C.dictionary encoding — A compression method storing repeated consecutive values as a value plus a count, paired with dictionary encoding in Parquet.
D.dictionary encoding — A maintenance operation that rewrites many small data files into fewer large ones to improve scan efficiency in lakes and open table formats.
17. Which term means: "Performance degradation from many tiny files that bloat metadata and scheduling overhead, mitigated by compaction into larger files."?
A.small files problem — Pushing filter conditions down to the storage layer so only matching row groups or files are read, using column statistics to skip data.
B.small files problem — Storing values of each column together so analytic scans read only needed columns and compress better, the basis of Parquet and ORC.
C.small files problem — Performance degradation from many tiny files that bloat metadata and scheduling overhead, mitigated by compaction into larger files.
D.small files problem — Hierarchical structures (structs, arrays, maps) that columnar formats like Parquet encode with repetition and definition levels.
20. Which term means: "A maintenance operation that rewrites many small data files into fewer large ones to improve scan efficiency in lakes and open table formats."?
A.compaction — A compression technique replacing repeated values with small integer codes referencing a dictionary, very effective for low-cardinality columns.
B.compaction — Pushing filter conditions down to the storage layer so only matching row groups or files are read, using column statistics to skip data.
C.compaction — A maintenance operation that rewrites many small data files into fewer large ones to improve scan efficiency in lakes and open table formats.
D.compaction — A row-oriented binary format with a self-describing schema, well suited to streaming and write-heavy workloads and schema evolution.
23. Which term means: "Applying structure to data at query time rather than at write time, the flexibility that lets lakes store raw semi-structured files."?
A.schema-on-read — A horizontal chunk of a Parquet file holding column chunks plus min/max statistics, the unit at which readers prune and parallelize.
B.schema-on-read — Storing values of each column together so analytic scans read only needed columns and compress better, the basis of Parquet and ORC.
C.schema-on-read — The smallest unit of storage and compression within a Parquet column chunk, holding encoded values, definition, and repetition levels.
D.schema-on-read — Applying structure to data at query time rather than at write time, the flexibility that lets lakes store raw semi-structured files.
26. Which term means: "A row-oriented binary format with a self-describing schema, well suited to streaming and write-heavy workloads and schema evolution."?
29. Which term means: "The smallest unit of storage and compression within a Parquet column chunk, holding encoded values, definition, and repetition levels."?
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