45 real Lineage & Governance 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 OpenLineage?
Mid
A.Lineage tracked at the granularity of individual columns, showing how each output field derives from specific upstream fields.
B.A regulatory requirement to delete an individual's personal data on request, driving designs that can locate and purge records by subject.
C.An open standard and API for collecting data lineage metadata (datasets, jobs, runs) across tools in a vendor-neutral way.
D.Rules defining how long data is kept before archival or deletion, balancing compliance, cost, and analytical need.
A.OpenLineage — An open standard and API for collecting data lineage metadata (datasets, jobs, runs) across tools in a vendor-neutral way.
B.OpenLineage — A governance pattern where sensitive columns are tagged and masking policies are applied based on tags, scaling policy enforcement across many tables.
C.OpenLineage — A formal agreement defining a dataset's schema, semantics, and quality guarantees between producer and consumer, enforced in the pipeline.
D.OpenLineage — A decentralized approach treating data as a product owned by domain teams, with federated governance and self-serve platform infrastructure.
5. Which term means: "Databricks' unified governance layer for data and AI assets providing centralized access control, lineage, and discovery across workspaces."?
A.Unity Catalog — A governance pattern where sensitive columns are tagged and masking policies are applied based on tags, scaling policy enforcement across many tables.
B.Unity Catalog — A lineage view tracing how each output column derives from specific upstream columns, enabling precise impact analysis of a change.
C.Unity Catalog — Databricks' unified governance layer for data and AI assets providing centralized access control, lineage, and discovery across workspaces.
D.Unity Catalog — Rules defining how long data is kept before archival or deletion, balancing compliance, cost, and analytical need.
A.column-level lineage — A governance feature filtering which rows a user can see based on their identity or attributes, enforced transparently at query time.
B.column-level lineage — Lineage tracked at the granularity of individual columns, showing how each output field derives from specific upstream fields.
C.column-level lineage — An open standard and API for emitting job, run, and dataset lineage events that lineage tools and catalogs can consume.
D.column-level lineage — A formal agreement defining a dataset's schema, semantics, and quality guarantees between producer and consumer, enforced in the pipeline.
11. Which term means: "A searchable inventory of data assets with metadata, ownership, descriptions, and lineage that helps users discover and trust data."?
A.data catalog — A formal agreement defining a dataset's schema, semantics, and quality guarantees between producer and consumer, enforced in the pipeline.
B.data catalog — A regulatory requirement to delete an individual's personal data on request, driving designs that can locate and purge records by subject.
C.data catalog — A searchable inventory of data assets with metadata, ownership, descriptions, and lineage that helps users discover and trust data.
D.data catalog — Data about data, such as schema, ownership, freshness, and lineage, that powers catalogs, governance, and discovery.
A.A formal agreement defining a dataset's schema, semantics, and quality guarantees between producer and consumer, enforced in the pipeline.
B.A searchable inventory of data assets with metadata, ownership, descriptions, and lineage that helps users discover and trust data.
C.A role accountable for the quality, definitions, and policy compliance of a data domain, often owning catalog entries and contracts.
D.A governance pattern where sensitive columns are tagged and masking policies are applied based on tags, scaling policy enforcement across many tables.
14. Which term means: "A governance pattern where sensitive columns are tagged and masking policies are applied based on tags, scaling policy enforcement across many tables."?
A.tag-based masking — Databricks' unified governance layer for data and AI assets providing centralized access control, lineage, and discovery across workspaces.
B.tag-based masking — A lineage view tracing how each output column derives from specific upstream columns, enabling precise impact analysis of a change.
C.tag-based masking — A governance pattern where sensitive columns are tagged and masking policies are applied based on tags, scaling policy enforcement across many tables.
D.tag-based masking — A decentralized approach treating data as a product owned by domain teams, with federated governance and self-serve platform infrastructure.
17. Which term means: "A role accountable for the quality, definitions, and policy compliance of a data domain, often owning catalog entries and contracts."?
A.data steward — Databricks' unified governance layer for data and AI assets providing centralized access control, lineage, and discovery across workspaces.
B.data steward — An open standard and API for collecting data lineage metadata (datasets, jobs, runs) across tools in a vendor-neutral way.
C.data steward — Rules defining how long data is kept before archival or deletion, balancing compliance, cost, and analytical need.
D.data steward — A role accountable for the quality, definitions, and policy compliance of a data domain, often owning catalog entries and contracts.
20. Which term means: "A decentralized approach treating data as a product owned by domain teams, with federated governance and self-serve platform infrastructure."?
A.data mesh — Data about data, such as schema, ownership, freshness, and lineage, that powers catalogs, governance, and discovery.
B.data mesh — Tagging columns that contain personally identifiable information so masking, retention, and access policies can be applied consistently.
C.data mesh — Rules defining how long data is kept before archival or deletion, balancing compliance, cost, and analytical need.
D.data mesh — A decentralized approach treating data as a product owned by domain teams, with federated governance and self-serve platform infrastructure.
23. Which term means: "Tagging columns that contain personally identifiable information so masking, retention, and access policies can be applied consistently."?
A.PII classification — An open standard and API for collecting data lineage metadata (datasets, jobs, runs) across tools in a vendor-neutral way.
B.PII classification — A formal agreement defining a dataset's schema, semantics, and quality guarantees between producer and consumer, enforced in the pipeline.
C.PII classification — Tagging columns that contain personally identifiable information so masking, retention, and access policies can be applied consistently.
D.PII classification — A governance feature filtering which rows a user can see based on their identity or attributes, enforced transparently at query time.
A.metadata — A role accountable for the quality, definitions, and policy compliance of a data domain, often owning catalog entries and contracts.
B.metadata — A formal agreement defining a dataset's schema, semantics, and quality guarantees between producer and consumer, enforced in the pipeline.
C.metadata — Data about data, such as schema, ownership, freshness, and lineage, that powers catalogs, governance, and discovery.
D.metadata — An open standard and API for collecting data lineage metadata (datasets, jobs, runs) across tools in a vendor-neutral way.
29. Which term means: "A governance feature filtering which rows a user can see based on their identity or attributes, enforced transparently at query time."?
A.row-level security — A governance feature filtering which rows a user can see based on their identity or attributes, enforced transparently at query time.
B.row-level security — A role accountable for the quality, definitions, and policy compliance of a data domain, often owning catalog entries and contracts.
C.row-level security — A decentralized approach treating data as a product owned by domain teams, with federated governance and self-serve platform infrastructure.
D.row-level security — A searchable inventory of data assets with metadata, ownership, descriptions, and lineage that helps users discover and trust data.
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