Question 31

A downstream Power BI report that consumes `analytics.gold.revenue_summary` started showing incorrect totals after a change to an upstream table. Before deleting a suspect column in `analytics.silver.transactions`, a data engineer must perform an impact analysis to identify every downstream table, job, notebook, and dashboard that depends on that column, and trace the upstream sources that feed `revenue_summary`. All queries ran on Azure Databricks compute attached to the Unity Catalog metastore. Which approach correctly uses Unity Catalog data lineage to satisfy this requirement?

• ALineage must be enabled and captured manually for each table; the engineer has to run `ANALYZE TABLE ... COMPUTE LINEAGE` first, because Unity Catalog does not capture lineage automatically.
• BIn Catalog Explorer, open the table's **Lineage** tab and **See Lineage Graph** to view upstream/downstream tables, click a column for column-level lineage, and filter the lineage details by notebooks, jobs, pipelines, queries, and dashboards — lineage is captured automatically down to the column level.
• CCatalog Explorer lineage shows only table-to-table relationships; to see which jobs, notebooks, or dashboards consume a table, the engineer must instead parse the Delta transaction log of each object manually.
• DLineage is only available through the REST API and cannot be visualized; Catalog Explorer has no lineage view.