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The action Set a retention policy consistent with the required Delta time-travel and recovery window solves the team wants to keep enough history for rollback before cleanup because aggressive vacuum can permanently remove old files necessary for historical versions; it does not by itself solve the company wants to reclaim storage occupied by old unreferenced files, whereas Use VACUUM with an approved retention threshold after reviewing time-travel needs does.

Choosing Delete the Delta transaction log because the table has too many small files would not satisfy the case condition (the company wants to reclaim storage occupied by old unreferenced files), because removing transaction metadata breaks consistency and is not file compaction; Use VACUUM with an approved retention threshold after reviewing time-travel needs preserves the intended result.

Although Evaluate V-order during compatible Delta maintenance with measured read and write tradeoffs fits the separate issue that direct Lake consumers read wide tables and an approved layout optimization may help because v-order can improve some analytic read patterns while adding write overhead, this case concerns the company wants to reclaim storage occupied by old unreferenced files and should be resolved with Use VACUUM with an approved retention threshold after reviewing time-travel needs.

For this requirement of developers need access to diagnose job runs but must not change production configuration, Change all dataset creation dates to force a scheduled Airflow run would be unsafe or ineffective because altering business data is not a valid way to repair orchestration scheduling; apply Separate read and operational duties using supported workspace and Airflow permissions instead.

Although Verify the deployment/synchronization path that makes DAG files available to the Airflow environment fits the separate issue that a DAG has been checked into source control but remains unavailable to the managed scheduler because git repository existence alone does not prove that a scheduler has loaded the DAG definition, this case concerns developers need access to diagnose job runs but must not change production configuration and should be resolved with Separate read and operational duties using supported workspace and Airflow permissions.

Option Review the DAG schedule and configured timezone rather than changing storage timestamps blindly would focus on the separate issue that a managed Airflow DAG loads a file at a time different from the documented time zone because airflow execution boundaries are driven by its schedule and timezone semantics, not by the time files were originally created, not the current requirement that developers need access to diagnose job runs but must not change production configuration; the appropriate remedy is Separate read and operational duties using supported workspace and Airflow permissions.

Choosing Disable all Fabric deployment review controls and overwrite every target item on each build would not satisfy the case condition (a notebook depends on a lakehouse item that was not included in the promotion), because blind wholesale deployment can replace unrelated production configuration; Verify dependency support and deploy the associated items in the correct stage context preserves the intended result.

Option Define and validate supported deployment rules and environment-specific parameters would focus on the separate issue that the test workspace references development-only resource identifiers because targets often require different connections or identifiers even when the item structure is promoted, not the current requirement that a notebook depends on a lakehouse item that was not included in the promotion; the appropriate remedy is Verify dependency support and deploy the associated items in the correct stage context.

The option Check whether the deployment moved definitions rather than the underlying data applies to the separate issue that production promotion succeeds but source data remains absent, because fabric deployment pipelines typically promote supported item metadata; they are not a general copy mechanism for lakehouse data; the current condition is a notebook depends on a lakehouse item that was not included in the promotion, which calls for Verify dependency support and deploy the associated items in the correct stage context.

The option Rely on role order to make the last assigned OneLake grant erase earlier access fails to meet two roles each permit different regions but the user belongs to both, given that grant-based permissions must be reviewed as an effective combined set, not a sequential deny list; Evaluate the combined row-security grants and remove unintended role membership is the correct scope of action.

The option Use supported OneLake ReadWrite grants scoped to the selected folder if the identity is eligible applies to the separate issue that a data steward wants write access to one folder while retaining read-only access elsewhere, because folder-scoped ReadWrite can limit where a restricted user can change files without granting workspace Contributor; the current condition is two roles each permit different regions but the user belongs to both, which calls for Evaluate the combined row-security grants and remove unintended role membership.

Review the supported OneLake role-combination constraints before making both assignments is useful when a role combines column restrictions with a row predicate and a conflicting second role, since some RLS and CLS combinations across roles are unsupported and need a coherent security design; however, the actual requirement is two roles each permit different regions but the user belongs to both, which calls for Evaluate the combined row-security grants and remove unintended role membership.

Option Separate raw landing from validation and use explicit failure handling would focus on the separate issue that the parsing task can fail while the raw input must remain retained because preserving raw evidence enables correction without guessing or regenerating original input, not the current requirement that a new export must trigger ingestion within minutes of file creation; the appropriate remedy is Choose the supported file-event trigger with an exact source and folder match.

The option Deduplicate using the source event or file key before committing output rows applies to the separate issue that some files arrive twice with the same business ID, because event-based orchestration does not itself guarantee exactly-once business writes; the current condition is a new export must trigger ingestion within minutes of file creation, which calls for Choose the supported file-event trigger with an exact source and folder match.

For this requirement of a new export must trigger ingestion within minutes of file creation, Delete all source files as soon as the trigger fires, before validation finishes would be unsafe or ineffective because losing raw input before confirmed processing prevents safe recovery; apply Choose the supported file-event trigger with an exact source and folder match instead.

Join each historical sale to whichever product row is newest today would not satisfy analysts must group historical sales by the product category valid at sale time because current-only joins can retroactively misstate past sales categories; the defensible response is Use a Type 2 dimension with effective date ranges and surrogate keys where historical attributes matter.

The action Resolve the product dimension key during fact loading solves new fact rows contain natural product IDs but the fact table needs dimension surrogate keys because surrogate key lookup attaches each measure to the intended dimension member and version; it does not by itself solve analysts must group historical sales by the product category valid at sale time, whereas Use a Type 2 dimension with effective date ranges and surrogate keys where historical attributes matter does.

For a name spelling correction should update the current product record without creating a new historical version, Apply a Type 1 attribute overwrite for the explicitly nonhistorical correction can be justified because not every attribute change requires an SCD2 version; the business tracking rule determines the approach; for this scenario involving analysts must group historical sales by the product category valid at sale time, the targeted response is Use a Type 2 dimension with effective date ranges and surrogate keys where historical attributes matter.

The action Use ROW_NUMBER() over the order key ordered by authoritative version and retain row 1 solves the input contains multiple updates per order and only the latest version should survive because a deterministic window rank distinguishes the latest update from older duplicates; it does not by itself solve a delete feed must remove orders that are no longer present, whereas Apply an explicit delete or tombstone reconciliation to the target does.

Applying Use SELECT DISTINCT on every column to repair missing order updates when a delete feed must remove orders that are no longer present would introduce a defect because dISTINCT removes only identical rows; it does not choose the authoritative version; a direct solution is Apply an explicit delete or tombstone reconciliation to the target.

Although GROUP BY merchant and business date with an explicit SUM of validated amounts fits the separate issue that daily totals should show one row per merchant and reporting date because relational grouping defines the output grain and computes aggregates across the intended rows, this case concerns a delete feed must remove orders that are no longer present and should be resolved with Apply an explicit delete or tombstone reconciliation to the target.

The action Inspect Eventstream source, destination status and event delivery diagnostics solves the source connector is active but no records reach the Eventhouse destination because source health alone does not establish that a configured destination receives and maps the events; it does not by itself solve the operations dashboard must count events by five-minute window, whereas Aggregate using a defined event-time window before alert evaluation does.

Applying Disable all Eventstream destinations to make the source connector healthy when the operations dashboard must count events by five-minute window would introduce a defect because removing sinks cannot deliver events to the intended analytics table; a direct solution is Aggregate using a defined event-time window before alert evaluation.

Although Apply appropriate parsing, enrichment or destination mapping for the actual schema fits the separate issue that sensor payload fields arrive as nested JSON and the destination expects typed columns because type-compatible mappings are required before event fields can be queried as intended, this case concerns the operations dashboard must count events by five-minute window and should be resolved with Aggregate using a defined event-time window before alert evaluation.

Applying Treat a canceled Dataflow Gen2 run as an automatic full rollback of every destination when a Power Query query failed while other dataflow queries refreshed successfully would introduce a defect because different write stages and destinations may have committed partial results; a direct solution is Inspect per-query Dataflow Gen2 refresh detail and the failed Applied Step.

Although Check destination transaction outcomes and partial writes before rerunning fits the separate issue that a refresh was canceled after some destination writes had started because a canceled refresh can leave committed destination records depending on timing and sink, this case concerns a Power Query query failed while other dataflow queries refreshed successfully and should be resolved with Inspect per-query Dataflow Gen2 refresh detail and the failed Applied Step.

The option Compare refresh-duration history and the step-level source query cost applies to the separate issue that the same dataflow takes progressively longer after adding a source join, because trend evidence can identify new transformation bottlenecks rather than assuming the gateway is at fault; the current condition is a Power Query query failed while other dataflow queries refreshed successfully, which calls for Inspect per-query Dataflow Gen2 refresh detail and the failed Applied Step.

Option Inspect filter predicate types and compare sample event payloads would focus on the separate issue that one filter drops all valid messages due to a wrongly typed condition because a filter can intentionally exclude every event when a field type or value is wrong, not the current requirement that burst traffic causes a growing backlog and late analytics results; the appropriate remedy is Review throughput limits, capacity and sink ingestion metrics before scaling.

The option Update the authorized Eventstream destination connection and retest delivery applies to the separate issue that a destination stops receiving events after its access credentials expire, because destination credentials are required independently of source health; the current condition is burst traffic causes a growing backlog and late analytics results, which calls for Review throughput limits, capacity and sink ingestion metrics before scaling.

Choosing Drop messages whose structure changed without retaining error or schema evidence would not satisfy the case condition (burst traffic causes a growing backlog and late analytics results), because silent discard hides source-contract regressions and prevents safe recovery; Review throughput limits, capacity and sink ingestion metrics before scaling preserves the intended result.

Assume external accelerated Delta shortcuts support every native materialized-view capability would not satisfy events arrive in many tiny bursts that increase ingestion and indexing cost because external KQL tables have feature limitations distinct from native Eventhouse tables; the defensible response is Review ingestion batching and capacity metrics using supported Eventhouse policies.

The option Apply time predicates and relevant filters early in KQL queries applies to the separate issue that kQL queries scan a very long event history to compute only last-hour faults, because restricting the scanned time and keys reduces unnecessary event processing; the current condition is events arrive in many tiny bursts that increase ingestion and indexing cost, which calls for Review ingestion batching and capacity metrics using supported Eventhouse policies.

Enable eligible query acceleration with a justified cache horizon is useful when the team queries an external Delta shortcut with slow cross-storage reads, since accelerated shortcut data can improve query speed by caching external records, but incurs resource cost; however, the actual requirement is events arrive in many tiny bursts that increase ingestion and indexing cost, which calls for Review ingestion batching and capacity metrics using supported Eventhouse policies.