Git repository access does not itself grant Databricks resource creation rights to CI automation. That option implements grant the deployment principal narrow target-workspace and resource permissions required by the bundle. It addresses a different problem; here the distinguishing need is to commit dependency versions alongside source and make CI build a reproducible release artifact.

Source-code unit tests alone cannot prove platform permissions and resource wiring are correct. That option implements run integration checks in a controlled target environment using the deployment principal and sample data. It addresses a different problem; here the distinguishing need is to commit dependency versions alongside source and make CI build a reproducible release artifact.

Declarative deployments establish a reproducible source of truth and reveal manual environment divergence. That option implements reconcile deployed resource configuration to version-controlled bundle definitions. It addresses a different problem; here the distinguishing need is to commit dependency versions alongside source and make CI build a reproducible release artifact.

This would test a different behavior, chiefly use approved generalization or aggregation that meets the documented disclosure threshold. Generalized results reduce identifiability while retaining intended aggregate analytical value. The specific evidence needed here is that validated attempts to read retained history cannot recover the erased records after the approved purge.

This would test a different behavior, chiefly tokenize or hash identifiers using an approved managed key/salt and protect the lookup or reversal mechanism. Controlled pseudonymization supports permitted linkage while reducing direct identity disclosure. The specific evidence needed here is that validated attempts to read retained history cannot recover the erased records after the approved purge.

This would test a different behavior, chiefly add sensitive-data detection and appropriate masking or quarantine before publishing trusted datasets. PII may appear outside declared identifier columns; discovery plus controls prevent leaking unexpected fields. The specific evidence needed here is that validated attempts to read retained history cannot recover the erased records after the approved purge.

Window frames compute rolling measures across related rows without collapsing the result like GROUP BY. That option implements use a Spark SQL window aggregate with an explicit ordered frame. It addresses a different problem; here the distinguishing need is to use SUM with a partitioned ordered cumulative window frame.

Ranking functions differ in their handling of ties and subsequent rank numbers. That option implements choose RANK or DENSE_RANK according to whether gaps after ties are acceptable. It addresses a different problem; here the distinguishing need is to use SUM with a partitioned ordered cumulative window frame.

LAG accesses a preceding row in a defined order without requiring an ambiguous self-join. That option implements use the LAG window function partitioned by entity and ordered by event sequence. It addresses a different problem; here the distinguishing need is to use SUM with a partitioned ordered cumulative window frame.

Job run conditions can trigger follow-up tasks based on upstream outcomes without masking the original failure. That option implements configure task run conditions and notifications for the intended failure path. It addresses a different problem; here the distinguishing need is to use a Lakeflow Jobs If/Else condition task driven by an explicit job parameter.

Explicit parameter plumbing avoids hidden notebook state and makes executions repeatable. That option implements declare job-level parameters and reference them in task parameters or supported task values. It addresses a different problem; here the distinguishing need is to use a Lakeflow Jobs If/Else condition task driven by an explicit job parameter.

Job task dependencies express ordering and parallelism without timing-based notebook sleeps. That option implements create a Lakeflow Job dependency graph with upstream tasks and a downstream publish task. It addresses a different problem; here the distinguishing need is to use a Lakeflow Jobs If/Else condition task driven by an explicit job parameter.

This would test a different behavior, chiefly use a data-quality query and threshold-based alert on the published table metrics. Operational success does not prove business completeness; measurable quality thresholds detect low-volume anomalies. The specific evidence needed here is that a persistent breach generates an actionable notification and repeated compliant evaluations do not page responders.

This would test a different behavior, chiefly define a scheduled Lakehouse alert on a measured quality metric from approved evaluation data. Lakehouse alerts can monitor query results for quality and operational thresholds in governed datasets. The specific evidence needed here is that a persistent breach generates an actionable notification and repeated compliant evaluations do not page responders.

This would test a different behavior, chiefly create a Lakehouse alert over a query that tests table freshness and route it to authorized recipients. Query-based alerts evaluate measurable data conditions rather than depending only on job failure notifications. The specific evidence needed here is that a persistent breach generates an actionable notification and repeated compliant evaluations do not page responders.

Logical row deletion alone may leave prior files or versions accessible until compliant purge steps finish. That option implements delete affected rows and perform retention-aware physical cleanup including permitted history removal. It addresses a different problem; here the distinguishing need is to add sensitive-data detection and appropriate masking or quarantine before publishing trusted datasets.

Generalized results reduce identifiability while retaining intended aggregate analytical value. That option implements use approved generalization or aggregation that meets the documented disclosure threshold. It addresses a different problem; here the distinguishing need is to add sensitive-data detection and appropriate masking or quarantine before publishing trusted datasets.

Controlled pseudonymization supports permitted linkage while reducing direct identity disclosure. That option implements tokenize or hash identifiers using an approved managed key/salt and protect the lookup or reversal mechanism. It addresses a different problem; here the distinguishing need is to add sensitive-data detection and appropriate masking or quarantine before publishing trusted datasets.

Programmatic run metadata supports automation and central dashboards without screen scraping. That option implements query the supported Jobs REST API or SDK for run and task state. It addresses a different problem; here the distinguishing need is to open Spark UI and query profile to examine stages, shuffle and task skew.

Supported automation interfaces expose resource state consistently for deployment and monitoring tools. That option implements use the Databricks CLI or SDK with approved permissions to list pipeline metadata and updates. It addresses a different problem; here the distinguishing need is to open Spark UI and query profile to examine stages, shuffle and task skew.

Operator-level profiling distinguishes inefficient data access from downstream aggregation work. That option implements inspect the SQL query profile for scan volumes, shuffles, joins and spill information. It addresses a different problem; here the distinguishing need is to open Spark UI and query profile to examine stages, shuffle and task skew.

This would test a different behavior, chiefly add contract tests for schema and critical numeric invariants before releasing data. Schema and invariant assertions catch semantic failures that a successful job run or row count cannot detect. The specific evidence needed here is that two different orders for one customer survive while retry duplicates of the same order are eliminated.

This would test a different behavior, chiefly route invalid records to a governed quarantine dataset with reason and source metadata. Quarantine preserves traceability and supports repair instead of silently deleting evidence. The specific evidence needed here is that two different orders for one customer survive while retry duplicates of the same order are eliminated.

This would test a different behavior, chiefly add explicit Lakeflow data-quality expectations and select fail/drop handling consistent with the SLA. Declarative expectations evaluate data-quality rules and distinguish invalid rows from valid published data. The specific evidence needed here is that two different orders for one customer survive while retry duplicates of the same order are eliminated.

This would test a different behavior, chiefly apply governed classification tags and inspect matching Unity Catalog metadata. Governed tags make classification searchable and reusable for data-discovery and policy automation. The specific evidence needed here is that the KPI's lineage graph identifies the expected silver source and the producing job or query.

This would test a different behavior, chiefly keep governed transformations and registered assets in traceable catalog-managed paths. Catalog lineage is most reliable when supported operations and data assets are governed consistently. The specific evidence needed here is that the KPI's lineage graph identifies the expected silver source and the producing job or query.

This would test a different behavior, chiefly publish accurate Unity Catalog table and column comments plus ownership metadata for governed datasets. Documented metadata and stewardship improve discoverability and prevent accidental use of the wrong asset. The specific evidence needed here is that the KPI's lineage graph identifies the expected silver source and the producing job or query.

Central ABAC reduces the ability of table owners to remove sensitive-data restrictions applied across assets. That option implements use centrally controlled ABAC policies instead of owner-managed table masks for uniform enforcement. It addresses a different problem; here the distinguishing need is to attach a Unity Catalog column mask with a suitable identity-aware masking function.

View definition alone is not a substitute for object permissions; access paths must be authorized explicitly. That option implements grant the appropriate access to the governed view and enforce required underlying rights and controls. It addresses a different problem; here the distinguishing need is to attach a Unity Catalog column mask with a suitable identity-aware masking function.

Row filters enforce row-level visibility on each query instead of trusting users to remember a WHERE clause. That option implements apply a Unity Catalog row-filter policy to the shared table with authorized division logic. It addresses a different problem; here the distinguishing need is to attach a Unity Catalog column mask with a suitable identity-aware masking function.

Controlled computation and output policies are needed to stop identifying raw contributor records. That option implements apply Clean Room query restrictions that limit outputs to permitted aggregate results. It addresses a different problem; here the distinguishing need is to use a Databricks Clean Room collaboration with controlled queries and output policies.

Federation queries supported external data in place rather than scheduling a full replicated ingestion job. That option implements configure Lakehouse Federation using a governed connection and foreign catalog. It addresses a different problem; here the distinguishing need is to use a Databricks Clean Room collaboration with controlled queries and output policies.

Federation is useful for in-place access, but repeated large remote transfers favor ingestion into optimized local tables. That option implements materialize a governed local Delta copy for frequent heavy analytics instead of relying on remote federation scans. It addresses a different problem; here the distinguishing need is to use a Databricks Clean Room collaboration with controlled queries and output policies.