72 real Data Quality 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 data quality dimensions?
Junior
A.A named collection of Great Expectations assertions about a dataset that together define its quality contract.
B.A check that a table's row count falls within an expected range or relative change, catching partial loads and runaway duplications.
A.data quality dimensions — A dbt test that feeds a model fixed mock rows and asserts the transformed output, validating logic independently of warehouse data.
C.data quality dimensions — A check verifying that foreign-key values in one table all exist in the referenced table, catching orphaned records.
D.data quality dimensions — A data-quality check that flags metrics deviating from their historical baseline rather than failing a fixed threshold, catching unexpected drifts.
A.schema drift — Automated monitoring that flags metrics (row counts, nulls, distributions) deviating from learned historical patterns, a pillar of data observability.
B.schema drift — An open-source Python framework for declaring data expectations (assertions) and validating datasets, producing data docs and validation results.
C.schema drift — an unexpected change in source schema that can break pipelines
D.schema drift — A data-quality check that flags metrics deviating from their historical baseline rather than failing a fixed threshold, catching unexpected drifts.
11. Which term means: "An open-source Python framework for declaring data expectations (assertions) and validating datasets, producing data docs and validation results."?
A.Great Expectations — an agreed schema/SLA between data producers and consumers
B.Great Expectations — An open-source Python framework for declaring data expectations (assertions) and validating datasets, producing data docs and validation results.
C.Great Expectations — The practice of monitoring data health across freshness, volume, schema, distribution, and lineage to detect and resolve issues quickly.
D.Great Expectations — A documented maximum acceptable staleness for a dataset, breached when its last update falls outside the agreed window.
14. Which term means: "A data-quality tool using a declarative checks language (SodaCL) to scan datasets for anomalies, freshness, and rule violations in pipelines."?
A.A check confirming a column or key has no duplicate values, protecting primary-key and grain assumptions.
B.A dbt test that feeds a model fixed mock rows and asserts the transformed output, validating logic independently of warehouse data.
C.A formal, versioned agreement between data producers and consumers specifying schema, semantics, and quality guarantees, enforced in CI or at ingestion.
D.an agreed schema/SLA between data producers and consumers
17. Which term means: "A formal, versioned agreement between data producers and consumers specifying schema, semantics, and quality guarantees, enforced in CI or at ingestion."?
A.data contract — Asserting that incoming data matches an expected structure (columns, types, nullability) before it is accepted into a pipeline.
B.data contract — Comparing aggregate totals between source and target (counts, sums) to confirm a pipeline neither dropped nor duplicated data.
C.data contract — A dbt test that feeds a model fixed mock rows and asserts the transformed output, validating logic independently of warehouse data.
D.data contract — A formal, versioned agreement between data producers and consumers specifying schema, semantics, and quality guarantees, enforced in CI or at ingestion.
20. Which term means: "A test asserting that the newest data in a table is within an acceptable age threshold, catching stalled or delayed pipelines."?
A.freshness check — A pipeline safeguard that halts downstream loads when quality checks fail, preventing bad data from propagating to consumers.
B.freshness check — A test asserting that the newest data in a table is within an acceptable age threshold, catching stalled or delayed pipelines.
C.freshness check — A check that a table's row count falls within an expected range or relative change, catching partial loads and runaway duplications.
D.freshness check — A declarative data-quality assertion written in SodaCL (such as row count, missing percent, or freshness) that runs against a dataset.
A.A reusable bundle pairing data batches with an expectation suite and actions, run to validate data and emit results in a pipeline.
B.an unexpected change in source schema that can break pipelines
C.Automated monitoring that flags metrics (row counts, nulls, distributions) deviating from learned historical patterns, a pillar of data observability.
D.A dbt test that feeds a model fixed mock rows and asserts the transformed output, validating logic independently of warehouse data.
23. Which term means: "Automated monitoring that flags metrics (row counts, nulls, distributions) deviating from learned historical patterns, a pillar of data observability."?
B.anomaly detection — A named collection of Great Expectations assertions about a dataset that together define its quality contract.
C.anomaly detection — Automated monitoring that flags metrics (row counts, nulls, distributions) deviating from learned historical patterns, a pillar of data observability.
D.anomaly detection — A documented maximum acceptable staleness for a dataset, breached when its last update falls outside the agreed window.
A.an unexpected change in source schema that can break pipelines
B.A check that a table's row count falls within an expected range or relative change, catching partial loads and runaway duplications.
C.Automated monitoring that flags metrics (row counts, nulls, distributions) deviating from learned historical patterns, a pillar of data observability.
D.A check verifying that foreign-key values in one table all exist in the referenced table, catching orphaned records.
A.referential integrity test — A check that a table's row count falls within an expected range or relative change, catching partial loads and runaway duplications.
B.referential integrity test — A check verifying that foreign-key values in one table all exist in the referenced table, catching orphaned records.
C.referential integrity test — A named collection of Great Expectations assertions about a dataset that together define its quality contract.
D.referential integrity test — Automated monitoring that flags metrics (row counts, nulls, distributions) deviating from learned historical patterns, a pillar of data observability.
29. Which term means: "The practice of monitoring data health across freshness, volume, schema, distribution, and lineage to detect and resolve issues quickly."?
A.data observability — A formal, versioned agreement between data producers and consumers specifying schema, semantics, and quality guarantees, enforced in CI or at ingestion.
B.data observability — A dbt test that feeds a model fixed mock rows and asserts the transformed output, validating logic independently of warehouse data.
C.data observability — A named collection of Great Expectations assertions about a dataset that together define its quality contract.
D.data observability — The practice of monitoring data health across freshness, volume, schema, distribution, and lineage to detect and resolve issues quickly.
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