Lineage & Governance interview questions

1. A.Lineage tracked at the granularity of individual columns, showing how each output field derives from specific upstream fields.
2. B.A regulatory requirement to delete an individual's personal data on request, driving designs that can locate and purge records by subject.
3. C.An open standard and API for collecting data lineage metadata (datasets, jobs, runs) across tools in a vendor-neutral way.
4. D.Rules defining how long data is kept before archival or deletion, balancing compliance, cost, and analytical need.

Answer + AI explanation with Pro

1. A.OpenLineage
2. B.PII classification
3. C.tag-based masking
4. D.column-level lineage

Answer + AI explanation with Pro

1. A.OpenLineage — An open standard and API for collecting data lineage metadata (datasets, jobs, runs) across tools in a vendor-neutral way.
2. B.OpenLineage — A governance pattern where sensitive columns are tagged and masking policies are applied based on tags, scaling policy enforcement across many tables.
3. C.OpenLineage — A formal agreement defining a dataset's schema, semantics, and quality guarantees between producer and consumer, enforced in the pipeline.
4. D.OpenLineage — A decentralized approach treating data as a product owned by domain teams, with federated governance and self-serve platform infrastructure.

Answer + AI explanation with Pro

1. A.Tagging columns that contain personally identifiable information so masking, retention, and access policies can be applied consistently.
2. B.A governance feature filtering which rows a user can see based on their identity or attributes, enforced transparently at query time.
3. C.Databricks' unified governance layer for data and AI assets providing centralized access control, lineage, and discovery across workspaces.
4. D.Data about data, such as schema, ownership, freshness, and lineage, that powers catalogs, governance, and discovery.

Answer + AI explanation with Pro

1. A.Unity Catalog
2. B.data mesh
3. C.column-level lineage
4. D.data steward

Answer + AI explanation with Pro

1. 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.
2. B.Unity Catalog — A lineage view tracing how each output column derives from specific upstream columns, enabling precise impact analysis of a change.
3. C.Unity Catalog — Databricks' unified governance layer for data and AI assets providing centralized access control, lineage, and discovery across workspaces.
4. D.Unity Catalog — Rules defining how long data is kept before archival or deletion, balancing compliance, cost, and analytical need.

Answer + AI explanation with Pro

1. A.A regulatory requirement to delete an individual's personal data on request, driving designs that can locate and purge records by subject.
2. B.A governance feature filtering which rows a user can see based on their identity or attributes, enforced transparently at query time.
3. C.A searchable inventory of data assets with metadata, ownership, descriptions, and lineage that helps users discover and trust data.
4. D.Lineage tracked at the granularity of individual columns, showing how each output field derives from specific upstream fields.

Answer + AI explanation with Pro

1. A.data retention policy
2. B.OpenLineage
3. C.column-level lineage
4. D.data steward

Answer + AI explanation with Pro

1. 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.
2. B.column-level lineage — Lineage tracked at the granularity of individual columns, showing how each output field derives from specific upstream fields.
3. C.column-level lineage — An open standard and API for emitting job, run, and dataset lineage events that lineage tools and catalogs can consume.
4. D.column-level lineage — A formal agreement defining a dataset's schema, semantics, and quality guarantees between producer and consumer, enforced in the pipeline.

Answer + AI explanation with Pro

1. A.An open standard and API for emitting job, run, and dataset lineage events that lineage tools and catalogs can consume.
2. B.A searchable inventory of data assets with metadata, ownership, descriptions, and lineage that helps users discover and trust data.
3. C.Data about data, such as schema, ownership, freshness, and lineage, that powers catalogs, governance, and discovery.
4. D.Rules defining how long data is kept before archival or deletion, balancing compliance, cost, and analytical need.

Answer + AI explanation with Pro

1. A.PII classification
2. B.Unity Catalog
3. C.data mesh
4. D.data catalog

Answer + AI explanation with Pro

1. A.data catalog — A formal agreement defining a dataset's schema, semantics, and quality guarantees between producer and consumer, enforced in the pipeline.
2. 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.
3. C.data catalog — A searchable inventory of data assets with metadata, ownership, descriptions, and lineage that helps users discover and trust data.
4. D.data catalog — Data about data, such as schema, ownership, freshness, and lineage, that powers catalogs, governance, and discovery.

Answer + AI explanation with Pro

1. A.A formal agreement defining a dataset's schema, semantics, and quality guarantees between producer and consumer, enforced in the pipeline.
2. B.A searchable inventory of data assets with metadata, ownership, descriptions, and lineage that helps users discover and trust data.
3. C.A role accountable for the quality, definitions, and policy compliance of a data domain, often owning catalog entries and contracts.
4. D.A governance pattern where sensitive columns are tagged and masking policies are applied based on tags, scaling policy enforcement across many tables.

Answer + AI explanation with Pro

1. A.metadata
2. B.tag-based masking
3. C.data contract
4. D.PII classification

Answer + AI explanation with Pro

1. A.tag-based masking — Databricks' unified governance layer for data and AI assets providing centralized access control, lineage, and discovery across workspaces.
2. B.tag-based masking — A lineage view tracing how each output column derives from specific upstream columns, enabling precise impact analysis of a change.
3. 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.
4. D.tag-based masking — A decentralized approach treating data as a product owned by domain teams, with federated governance and self-serve platform infrastructure.

Answer + AI explanation with Pro

1. A.A role accountable for the quality, definitions, and policy compliance of a data domain, often owning catalog entries and contracts.
2. B.A searchable inventory of data assets with metadata, ownership, descriptions, and lineage that helps users discover and trust data.
3. C.Data about data, such as schema, ownership, freshness, and lineage, that powers catalogs, governance, and discovery.
4. D.Tagging columns that contain personally identifiable information so masking, retention, and access policies can be applied consistently.

Answer + AI explanation with Pro

1. A.data steward
2. B.GDPR right to erasure
3. C.OpenLineage
4. D.data catalog

Answer + AI explanation with Pro

1. A.data steward — Databricks' unified governance layer for data and AI assets providing centralized access control, lineage, and discovery across workspaces.
2. B.data steward — An open standard and API for collecting data lineage metadata (datasets, jobs, runs) across tools in a vendor-neutral way.
3. C.data steward — Rules defining how long data is kept before archival or deletion, balancing compliance, cost, and analytical need.
4. D.data steward — A role accountable for the quality, definitions, and policy compliance of a data domain, often owning catalog entries and contracts.

Answer + AI explanation with Pro

1. A.A decentralized approach treating data as a product owned by domain teams, with federated governance and self-serve platform infrastructure.
2. B.A governance feature filtering which rows a user can see based on their identity or attributes, enforced transparently at query time.
3. C.A lineage view tracing how each output column derives from specific upstream columns, enabling precise impact analysis of a change.
4. D.A role accountable for the quality, definitions, and policy compliance of a data domain, often owning catalog entries and contracts.

Answer + AI explanation with Pro

1. A.PII classification
2. B.tag-based masking
3. C.data mesh
4. D.data retention policy

Answer + AI explanation with Pro

1. A.data mesh — Data about data, such as schema, ownership, freshness, and lineage, that powers catalogs, governance, and discovery.
2. B.data mesh — Tagging columns that contain personally identifiable information so masking, retention, and access policies can be applied consistently.
3. C.data mesh — Rules defining how long data is kept before archival or deletion, balancing compliance, cost, and analytical need.
4. D.data mesh — A decentralized approach treating data as a product owned by domain teams, with federated governance and self-serve platform infrastructure.

Answer + AI explanation with Pro

1. A.Lineage tracked at the granularity of individual columns, showing how each output field derives from specific upstream fields.
2. B.Databricks' unified governance layer for data and AI assets providing centralized access control, lineage, and discovery across workspaces.
3. C.Tagging columns that contain personally identifiable information so masking, retention, and access policies can be applied consistently.
4. D.An open standard and API for emitting job, run, and dataset lineage events that lineage tools and catalogs can consume.

Answer + AI explanation with Pro

1. A.data mesh
2. B.metadata
3. C.PII classification
4. D.tag-based masking

Answer + AI explanation with Pro

1. A.PII classification — An open standard and API for collecting data lineage metadata (datasets, jobs, runs) across tools in a vendor-neutral way.
2. B.PII classification — A formal agreement defining a dataset's schema, semantics, and quality guarantees between producer and consumer, enforced in the pipeline.
3. C.PII classification — Tagging columns that contain personally identifiable information so masking, retention, and access policies can be applied consistently.
4. D.PII classification — A governance feature filtering which rows a user can see based on their identity or attributes, enforced transparently at query time.

Answer + AI explanation with Pro

1. A.A decentralized approach treating data as a product owned by domain teams, with federated governance and self-serve platform infrastructure.
2. B.Data about data, such as schema, ownership, freshness, and lineage, that powers catalogs, governance, and discovery.
3. C.A regulatory requirement to delete an individual's personal data on request, driving designs that can locate and purge records by subject.
4. D.A role accountable for the quality, definitions, and policy compliance of a data domain, often owning catalog entries and contracts.

Answer + AI explanation with Pro

1. A.data mesh
2. B.metadata
3. C.column-level lineage
4. D.PII classification

Answer + AI explanation with Pro

1. A.metadata — A role accountable for the quality, definitions, and policy compliance of a data domain, often owning catalog entries and contracts.
2. B.metadata — A formal agreement defining a dataset's schema, semantics, and quality guarantees between producer and consumer, enforced in the pipeline.
3. C.metadata — Data about data, such as schema, ownership, freshness, and lineage, that powers catalogs, governance, and discovery.
4. D.metadata — An open standard and API for collecting data lineage metadata (datasets, jobs, runs) across tools in a vendor-neutral way.

Answer + AI explanation with Pro

1. A.A governance feature filtering which rows a user can see based on their identity or attributes, enforced transparently at query time.
2. B.A decentralized approach treating data as a product owned by domain teams, with federated governance and self-serve platform infrastructure.
3. C.A formal agreement defining a dataset's schema, semantics, and quality guarantees between producer and consumer, enforced in the pipeline.
4. D.Data about data, such as schema, ownership, freshness, and lineage, that powers catalogs, governance, and discovery.

Answer + AI explanation with Pro

1. A.row-level security
2. B.data mesh
3. C.metadata
4. D.data retention policy

Answer + AI explanation with Pro

1. 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.
2. B.row-level security — A role accountable for the quality, definitions, and policy compliance of a data domain, often owning catalog entries and contracts.
3. C.row-level security — A decentralized approach treating data as a product owned by domain teams, with federated governance and self-serve platform infrastructure.
4. D.row-level security — A searchable inventory of data assets with metadata, ownership, descriptions, and lineage that helps users discover and trust data.

Answer + AI explanation with Pro