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That observation is a valid indicator for “The alert query produces no row because ingestion failed and this must be distinguished from zero” when using Define explicit null/no-data handling in the alert query and condition. The scenario instead tests “A new pipeline produces no useful quality metrics for rejected data”; the relevant result is Test invalid rows create observable expectation metrics in event logs.

The arrival rate exceeds processing capacity during the backlog window would demonstrate the separate action Compare input rate and processed-rows rate in streaming progress, which addresses “A stream seems healthy but event processing falls farther behind incoming data.” It does not verify the requirement “A new pipeline produces no useful quality metrics for rejected data” following Add supported expectations and inspect corresponding event metrics.

Historic pipeline updates can be filtered by ID and timestamp would demonstrate the separate action Expose or query the supported event-log table through SQL, which addresses “An engineer needs a persistent queryable audit of pipeline events rather than dashboard screenshots.” It does not verify the requirement “A new pipeline produces no useful quality metrics for rejected data” following Add supported expectations and inspect corresponding event metrics.

Use supported job run/system observability tables or Jobs API belongs to the separate case “Operators need to track Lakeflow Jobs historical run states and durations,” where Historical runs report success/failure and timing for the target job would be expected. That differs from the condition in this stem: “FinOps must allocate Databricks DBU consumption by job and cost center.”

Query supported query-history or workload system data belongs to the separate case “A team needs to attribute expensive SQL warehouse queries to their owners,” where An identified query and principal correlate with high resource consumption would be expected. That differs from the condition in this stem: “FinOps must allocate Databricks DBU consumption by job and cost center.”

Query the available audit system table for the relevant securable and action belongs to the separate case “Security needs to investigate which principal changed protected catalog permissions,” where The event identifies actor, action and resource with timestamp would be expected. That differs from the condition in this stem: “FinOps must allocate Databricks DBU consumption by job and cost center.”

This signal belongs to the adjacent case “Operators need to track Lakeflow Jobs historical run states and durations,” whose remedy is Use supported job run/system observability tables or Jobs API. The observed result for the present requirement is Aggregated usage by job or tag matches the reported consumption records.

That observation is a valid indicator for “A team needs to attribute expensive SQL warehouse queries to their owners” when using Query supported query-history or workload system data. The scenario instead tests “FinOps must allocate Databricks DBU consumption by job and cost center”; the relevant result is Aggregated usage by job or tag matches the reported consumption records.

This signal belongs to the adjacent case “Security needs to investigate which principal changed protected catalog permissions,” whose remedy is Query the available audit system table for the relevant securable and action. The observed result for the present requirement is Aggregated usage by job or tag matches the reported consumption records.

This action solves a different issue: “A team wants to forecast future monthly costs from observed daily spend.” Its normal signal is The report separates observed spend from extrapolated future cost. Here the requested behavior is “Security needs to investigate which principal changed protected catalog permissions,” so that signal is not sufficient.

This action solves a different issue: “Operators need to track Lakeflow Jobs historical run states and durations.” Its normal signal is Historical runs report success/failure and timing for the target job. Here the requested behavior is “Security needs to investigate which principal changed protected catalog permissions,” so that signal is not sufficient.

Query supported query-history or workload system data is appropriate when “A team needs to attribute expensive SQL warehouse queries to their owners”; it would be verified by An identified query and principal correlate with high resource consumption. It does not directly satisfy “Security needs to investigate which principal changed protected catalog permissions,” whose relevant acceptance evidence is The event identifies actor, action and resource with timestamp.

That observation is a valid indicator for “FinOps must allocate Databricks DBU consumption by job and cost center” when using Query the account's billing usage system tables and associated job metadata. The scenario instead tests “Security needs to investigate which principal changed protected catalog permissions”; the relevant result is The event identifies actor, action and resource with timestamp.

The report separates observed spend from extrapolated future cost pertains to a different control (Aggregate recent system billing usage and apply a documented forecast assumption) and a different need (A team wants to forecast future monthly costs from observed daily spend). It cannot establish that Query the available audit system table for the relevant securable and action met the stated goal.

Historical runs report success/failure and timing for the target job would demonstrate the separate action Use supported job run/system observability tables or Jobs API, which addresses “Operators need to track Lakeflow Jobs historical run states and durations.” It does not verify the requirement “Security needs to investigate which principal changed protected catalog permissions” following Query the available audit system table for the relevant securable and action.

Query the available audit system table for the relevant securable and action belongs to the separate case “Security needs to investigate which principal changed protected catalog permissions,” where The event identifies actor, action and resource with timestamp would be expected. That differs from the condition in this stem: “A team needs to attribute expensive SQL warehouse queries to their owners.”

The alternative Query the account's billing usage system tables and associated job metadata has a distinct purpose—FinOps must allocate Databricks DBU consumption by job and cost center. It would not establish the required outcome (A team needs to attribute expensive SQL warehouse queries to their owners); the necessary verification concerns An identified query and principal correlate with high resource consumption.

Aggregate recent system billing usage and apply a documented forecast assumption is appropriate when “A team wants to forecast future monthly costs from observed daily spend”; it would be verified by The report separates observed spend from extrapolated future cost. It does not directly satisfy “A team needs to attribute expensive SQL warehouse queries to their owners,” whose relevant acceptance evidence is An identified query and principal correlate with high resource consumption.

This signal belongs to the adjacent case “Security needs to investigate which principal changed protected catalog permissions,” whose remedy is Query the available audit system table for the relevant securable and action. The observed result for the present requirement is An identified query and principal correlate with high resource consumption.

The report separates observed spend from extrapolated future cost would demonstrate the separate action Aggregate recent system billing usage and apply a documented forecast assumption, which addresses “A team wants to forecast future monthly costs from observed daily spend.” It does not verify the requirement “A team needs to attribute expensive SQL warehouse queries to their owners” following Query supported query-history or workload system data.

That observation is a valid indicator for “Operators need to track Lakeflow Jobs historical run states and durations” when using Use supported job run/system observability tables or Jobs API. The scenario instead tests “A team needs to attribute expensive SQL warehouse queries to their owners”; the relevant result is An identified query and principal correlate with high resource consumption.

The alternative Aggregate recent system billing usage and apply a documented forecast assumption has a distinct purpose—A team wants to forecast future monthly costs from observed daily spend. It would not establish the required outcome (Operators need to track Lakeflow Jobs historical run states and durations); the necessary verification concerns Historical runs report success/failure and timing for the target job.

Query the account's billing usage system tables and associated job metadata is appropriate when “FinOps must allocate Databricks DBU consumption by job and cost center”; it would be verified by Aggregated usage by job or tag matches the reported consumption records. It does not directly satisfy “Operators need to track Lakeflow Jobs historical run states and durations,” whose relevant acceptance evidence is Historical runs report success/failure and timing for the target job.

Query the available audit system table for the relevant securable and action is appropriate when “Security needs to investigate which principal changed protected catalog permissions”; it would be verified by The event identifies actor, action and resource with timestamp. It does not directly satisfy “Operators need to track Lakeflow Jobs historical run states and durations,” whose relevant acceptance evidence is Historical runs report success/failure and timing for the target job.

The event identifies actor, action and resource with timestamp would demonstrate the separate action Query the available audit system table for the relevant securable and action, which addresses “Security needs to investigate which principal changed protected catalog permissions.” It does not verify the requirement “Operators need to track Lakeflow Jobs historical run states and durations” following Use supported job run/system observability tables or Jobs API.

That observation is a valid indicator for “FinOps must allocate Databricks DBU consumption by job and cost center” when using Query the account's billing usage system tables and associated job metadata. The scenario instead tests “Operators need to track Lakeflow Jobs historical run states and durations”; the relevant result is Historical runs report success/failure and timing for the target job.

An identified query and principal correlate with high resource consumption pertains to a different control (Query supported query-history or workload system data) and a different need (A team needs to attribute expensive SQL warehouse queries to their owners). It cannot establish that Use supported job run/system observability tables or Jobs API met the stated goal.

The alternative Use supported job run/system observability tables or Jobs API has a distinct purpose—Operators need to track Lakeflow Jobs historical run states and durations. It would not establish the required outcome (A team wants to forecast future monthly costs from observed daily spend); the necessary verification concerns The report separates observed spend from extrapolated future cost.

This action solves a different issue: “Security needs to investigate which principal changed protected catalog permissions.” Its normal signal is The event identifies actor, action and resource with timestamp. Here the requested behavior is “A team wants to forecast future monthly costs from observed daily spend,” so that signal is not sufficient.

Query supported query-history or workload system data is appropriate when “A team needs to attribute expensive SQL warehouse queries to their owners”; it would be verified by An identified query and principal correlate with high resource consumption. It does not directly satisfy “A team wants to forecast future monthly costs from observed daily spend,” whose relevant acceptance evidence is The report separates observed spend from extrapolated future cost.

An identified query and principal correlate with high resource consumption pertains to a different control (Query supported query-history or workload system data) and a different need (A team needs to attribute expensive SQL warehouse queries to their owners). It cannot establish that Aggregate recent system billing usage and apply a documented forecast assumption met the stated goal.

Aggregated usage by job or tag matches the reported consumption records would demonstrate the separate action Query the account's billing usage system tables and associated job metadata, which addresses “FinOps must allocate Databricks DBU consumption by job and cost center.” It does not verify the requirement “A team wants to forecast future monthly costs from observed daily spend” following Aggregate recent system billing usage and apply a documented forecast assumption.

Historical runs report success/failure and timing for the target job pertains to a different control (Use supported job run/system observability tables or Jobs API) and a different need (Operators need to track Lakeflow Jobs historical run states and durations). It cannot establish that Aggregate recent system billing usage and apply a documented forecast assumption met the stated goal.