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Option Run a supported Delta OPTIMIZE or compaction operation on the affected table would focus on the separate issue that query startup time rises as new micro-batches produce many small files because compaction reduces the number of small files read and managed by the query engine, not the current requirement that the team wants to keep enough history for rollback before cleanup; the appropriate remedy is Set a retention policy consistent with the required Delta time-travel and recovery window.

Choosing Delete the Delta transaction log because the table has too many small files would not satisfy the case condition (the team wants to keep enough history for rollback before cleanup), because removing transaction metadata breaks consistency and is not file compaction; Set a retention policy consistent with the required Delta time-travel and recovery window preserves the intended result.

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

Inspect the notebook's attached environment and its published dependency version is useful when analysts cannot identify which environment supplied a notebook's Python package, since notebook behavior depends on the assigned environment, not only the source code visible in a cell; however, the actual requirement is runtime changes must not silently alter the behavior of month-end financial aggregations, which calls for Test the new runtime in a separate workspace and validate representative totals before promoting it.

Applying Disable the capacity workload monitoring reports so the team's Spark jobs can run faster when runtime changes must not silently alter the behavior of month-end financial aggregations would introduce a defect because removing observability does not increase available processing resources; a direct solution is Test the new runtime in a separate workspace and validate representative totals before promoting it.

For workloads suddenly consume more Spark executors than the operations budget permits, Review supported Spark autoscale and pool limits with recorded peak job demand can be justified because the Spark pool's resource policy should reflect measured concurrency rather than assuming limitless executor capacity; for this scenario involving runtime changes must not silently alter the behavior of month-end financial aggregations, the targeted response is Test the new runtime in a separate workspace and validate representative totals before promoting it.

Applying Publish every local notebook runtime token in Git to ensure colleagues can replay its session when the workspace is connected to a branch that is about to be deleted would introduce a defect because runtime credentials must not be committed as deployment inputs; a direct solution is Repoint Git integration to the approved durable branch and verify sync state.

For a new feature is ready for review but not for production, Commit to a reviewed branch and promote through test rather than syncing into production directly can be justified because branch and stage separation limits exposure of unvalidated Fabric definitions; for this scenario involving the workspace is connected to a branch that is about to be deleted, the targeted response is Repoint Git integration to the approved durable branch and verify sync state.

Although Remove the secret from source, rotate it and reference an approved secret store fits the separate issue that a confidential parameter appears in version-controlled notebook metadata because deleting the visible commit alone is insufficient when the credential may remain in repository history, this case concerns the workspace is connected to a branch that is about to be deleted and should be resolved with Repoint Git integration to the approved durable branch and verify sync state.

The action Implement a supported row-level security predicate or secure view based on verified identities solves a warehouse view exposes too many rows to regional analysts because row-based query enforcement is needed to constrain which business records are returned; it does not by itself solve a team must be prevented from querying the raw customer-email column at all, whereas Use column-level authorization rather than relying only on dynamic masking does.

For a privileged analyst sees unmasked values during a security test, Inspect role-based unmask privileges and test with a genuinely restricted identity can be justified because dynamic masking normally permits appropriately privileged principals to see full values, so the test principal matters; for this scenario involving a team must be prevented from querying the raw customer-email column at all, the targeted response is Use column-level authorization rather than relying only on dynamic masking.

For this requirement of a team must be prevented from querying the raw customer-email column at all, Treat a masked email display as proof that users have no access to any sensitive row or other column would be unsafe or ineffective because presentation masking does not automatically enforce row and object restrictions; apply Use column-level authorization rather than relying only on dynamic masking instead.

The option Treat every failed notebook activity as success so the next step always runs fails to meet a pipeline should invoke the notebook only after Copy activity completes successfully, given that concealing failure would produce incomplete or misleading downstream datasets; Configure an On Success activity dependency from Copy to the notebook is the correct scope of action.

Option Pass flight batch ID and source path as pipeline and notebook parameters would focus on the separate issue that operators must rerun one arrival without processing all earlier manifests because stable input identifiers enable deterministic reruns without hard-coded source filenames, not the current requirement that a pipeline should invoke the notebook only after Copy activity completes successfully; the appropriate remedy is Configure an On Success activity dependency from Copy to the notebook.

The option Configure failure-handling dependencies and a supported notification path applies to the separate issue that a failed notebook should produce an explicit operational alert instead of leaving a silent gap, because a failed transform needs an observable failure state for recovery and SLA management; the current condition is a pipeline should invoke the notebook only after Copy activity completes successfully, which calls for Configure an On Success activity dependency from Copy to the notebook.

Although Reconcile late corrections and rebuild the affected daily stock projection fits the separate issue that upstream corrections arrive after an end-of-day partition was processed because a finalized partition can still require repair when late source changes are valid, this case concerns daily stock balances should reflect the latest valid reading for each SKU and location and should be resolved with Use keyed latest-state upsert with the source's authoritative version.

For this requirement of daily stock balances should reflect the latest valid reading for each SKU and location, Merge stock balances by description text without SKU and location keys would be unsafe or ineffective because display labels are not stable business identifiers for mutable inventory records; apply Use keyed latest-state upsert with the source's authoritative version instead.

The option Retain immutable line-level raw or curated events alongside the balance projection applies to the separate issue that order line events must remain recoverable after being aggregated into stock totals, because the current balance cannot reconstruct all historical business movements by itself; the current condition is daily stock balances should reflect the latest valid reading for each SKU and location, which calls for Use keyed latest-state upsert with the source's authoritative version.

The option Land the original JSON under the Files area before normalization applies to the separate issue that raw JSON vendor feeds should be retained without forcing all fields into a curated schema, because the Files area supports raw formats while curated Delta tables can be derived later; the current condition is analysts want to query the curated Delta table using the lakehouse SQL analytics endpoint, which calls for Publish a supported Delta table and verify endpoint visibility and query permissions.

Store the curated output as a Delta table in the lakehouse Tables area is useful when engineers require a transactional table supporting reliable updates and merges, since delta provides a transaction log and table semantics beyond standalone Parquet files; however, the actual requirement is analysts want to query the curated Delta table using the lakehouse SQL analytics endpoint, which calls for Publish a supported Delta table and verify endpoint visibility and query permissions.

The option Treat a single Parquet data file with no Delta log as a fully transactional Delta table fails to meet analysts want to query the curated Delta table using the lakehouse SQL analytics endpoint, given that transactional merge behavior requires a valid Delta table structure; Publish a supported Delta table and verify endpoint visibility and query permissions is the correct scope of action.

The option Join normalized terminal IDs to the authorized reference set applies to the separate issue that fraud signals must be matched with a known list of high-risk terminals, because a key-based join enriches event observations without relying on display-name matching; the current condition is a duplicate delivery has the same transaction ID and source revision, which calls for Deduplicate according to transaction ID and authoritative revision before counting outcomes.

Applying Delete all declined events from the source to improve the approval rate shown to management when a duplicate delivery has the same transaction ID and source revision would introduce a defect because removing actual business outcomes falsifies the analytical record; a direct solution is Deduplicate according to transaction ID and authoritative revision before counting outcomes.

For some source timestamps are strings with inconsistent formats, Parse timestamps into KQL datetime values before binning or ordering can be justified because typed event time is necessary for accurate windows and chronology; for this scenario involving a duplicate delivery has the same transaction ID and source revision, the targeted response is Deduplicate according to transaction ID and authoritative revision before counting outcomes.

Inspect execution history and the query's plan or resource metrics is useful when a T-SQL query is canceled after exceeding its execution window, since sQL job cancellation should be diagnosed from the affected statement and environment; however, the actual requirement is the team needs to detect recurring threshold breaches, which calls for Define supported alert thresholds over relevant query and pipeline run metrics.

The option Hide long-running statements from monitoring so the warehouse appears within SLA fails to meet the team needs to detect recurring threshold breaches, given that concealing execution history does not resolve performance or correctness; Define supported alert thresholds over relevant query and pipeline run metrics is the correct scope of action.

For the SQL load succeeds but a downstream report remains stale, Inspect the semantic refresh dependency and its recorded completion state can be justified because source execution and report data availability are different lifecycle steps; for this scenario involving the team needs to detect recurring threshold breaches, the targeted response is Define supported alert thresholds over relevant query and pipeline run metrics.

The action Inspect the activity's actual JSON output and correct the dynamic expression solves sQL completes but a pipeline expression references an absent activity output property because pipeline expressions must match supported output fields from the upstream activity; it does not by itself solve a notebook activity fails after the SQL load because it references an old table name, whereas Update the notebook's source reference and stage-specific binding does.

Rename all source tables to match a typo in one downstream activity without reviewing dependencies would not satisfy a notebook activity fails after the SQL load because it references an old table name because uncontrolled schema renames can break many valid consumers; the defensible response is Update the notebook's source reference and stage-specific binding.

Although Make the SQL target write idempotent and control replay boundaries fits the separate issue that the entire run retries after one downstream failure and duplicates accepted SQL rows because retrying a graph cannot assume all prior activities had no side effects, this case concerns a notebook activity fails after the SQL load because it references an old table name and should be resolved with Update the notebook's source reference and stage-specific binding.

Although Evaluate a supported incremental or partitioned source load strategy fits the separate issue that dataflow Gen2 refresh repeatedly processes all historical rows despite a small change set because matching extraction to change volume reduces unnecessary transformation work, this case concerns multiple tiny pipeline runs spend more time starting than moving data and should be resolved with Combine compatible small loads into efficient batches with bounded orchestration.

Choosing Overwrite source history during dataflow tuning to avoid reading large input would not satisfy the case condition (multiple tiny pipeline runs spend more time starting than moving data), because performance changes should preserve source data and business correctness; Combine compatible small loads into efficient batches with bounded orchestration preserves the intended result.

The option Review supported Power Query folding and move eligible filters nearer to the source applies to the separate issue that a Dataflow query contains transformations that prevent effective source pushdown, because query folding can reduce transferred rows when the connector and transform allow it; the current condition is multiple tiny pipeline runs spend more time starting than moving data, which calls for Combine compatible small loads into efficient batches with bounded orchestration.