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Choosing Turn on unrestricted workspace access as a prerequisite for changing a business domain would not satisfy the case condition (the domain owner wants to move a sensitive workspace but keep data access unchanged), because domain administration does not require making confidential workspace items publicly readable; Reassign the domain and verify that workspace role and item ACLs remain as intended preserves the intended result.

Although Apply meaningful item metadata and endorsement separately from domain grouping fits the separate issue that a catalog search cannot distinguish approved datasets from experiments in the same domain because domain scope describes organization, while endorsement conveys an asset's verified readiness for wider use, this case concerns the domain owner wants to move a sensitive workspace but keep data access unchanged and should be resolved with Reassign the domain and verify that workspace role and item ACLs remain as intended.

Option Use appropriate domain or subdomain structures with delegated owners would focus on the separate issue that two subteams need separate stewardship without creating entirely separate Fabric tenants because domain hierarchy supports ownership organization while workspace and item permissions remain independent, not the current requirement that the domain owner wants to move a sensitive workspace but keep data access unchanged; the appropriate remedy is Reassign the domain and verify that workspace role and item ACLs remain as intended.

Choosing Assume Git integration automatically backs up all OneLake source files and credentials would not satisfy the case condition (production item definitions must be recreated for disaster recovery), because definition versioning is not a replacement for storage backup or secret management; Preserve reviewed source revisions and repeatable deployment configuration preserves the intended result.

Although Use feature branches with peer review, then controlled stage promotion fits the separate issue that the team needs to isolate unreviewed code from published notebooks because branch discipline prevents in-progress notebook logic from reaching operational workloads accidentally, this case concerns production item definitions must be recreated for disaster recovery and should be resolved with Preserve reviewed source revisions and repeatable deployment configuration.

Option Parameterize the environment-specific lakehouse binding and test after stage promotion would focus on the separate issue that a notebook references a workspace-specific lakehouse ID embedded in a cell because hard-coded development IDs can make production notebooks read the wrong item, not the current requirement that production item definitions must be recreated for disaster recovery; the appropriate remedy is Preserve reviewed source revisions and repeatable deployment configuration.

Although Assign an appropriately scoped authoring workspace role and verify least privilege fits the separate issue that a small engineering group needs permission to create and modify workspace data pipelines because editing item definitions requires a role allowing contribution, not only read permission, this case concerns analysts should read selected reports but not edit any workspace resources and should be resolved with Assign a suitable Viewer workspace role and separately verify item data permissions.

The option Share the particular supported item with the user using its item-level permissions applies to the separate issue that one specific analyst needs access to a report without being granted the whole workspace, because item-level sharing can narrow the permission scope compared with a broad workspace role; the current condition is analysts should read selected reports but not edit any workspace resources, which calls for Assign a suitable Viewer workspace role and separately verify item data permissions.

For this requirement of analysts should read selected reports but not edit any workspace resources, Make external contractors Workspace Admin so they can view one monthly chart would be unsafe or ineffective because admin grants broader management access than needed to consume a single report; apply Assign a suitable Viewer workspace role and separately verify item data permissions instead.

The option Allow every external OneLake event to trigger a privileged raw-data overwrite fails to meet operators need to diagnose why a file did not launch a pipeline run, given that unfiltered events can cause unauthorized or duplicate destructive work; Inspect event subscription status, source path filters and the trigger's run evidence is the correct scope of action.

The option Configure an event-based pipeline trigger for the relevant OneLake file event applies to the separate issue that the pipeline should start when an approved file arrives instead of polling once a day, because event triggers react to observed storage events rather than a fixed periodic clock; the current condition is operators need to diagnose why a file did not launch a pipeline run, which calls for Inspect event subscription status, source path filters and the trigger's run evidence.

Filter event trigger scope by supported path and event type is useful when only files under an agreed incoming folder should launch processing, since broad event subscriptions can run the wrong pipeline for unrelated storage changes; however, the actual requirement is operators need to diagnose why a file did not launch a pipeline run, which calls for Inspect event subscription status, source path filters and the trigger's run evidence.

The option Verify the persisted source offset and reconcile a bounded missed-event window applies to the separate issue that a CDC connector starts at an incorrect log offset after maintenance, because correctness depends on continuity across the source change stream, not merely connector uptime; the current condition is the target must capture inserts, updates and deletes without a full daily scan, which calls for Configure a supported CDC-based incremental ingestion path with operation types.

For this requirement of the target must capture inserts, updates and deletes without a full daily scan, Ignore delete operations because they use less storage than inserts would be unsafe or ineffective because missing tombstones can leave closed or removed accounts incorrectly active; apply Configure a supported CDC-based incremental ingestion path with operation types instead.

For cDC events can arrive twice after connector failover, Enforce event-position idempotency in the target application step can be justified because duplicate commit positions should not apply the same source mutation twice; for this scenario involving the target must capture inserts, updates and deletes without a full daily scan, the targeted response is Configure a supported CDC-based incremental ingestion path with operation types.

For this requirement of sensors report temperatures in both Fahrenheit and Celsius, Compare Fahrenheit and Celsius values directly and label any mismatch a sensor defect would be unsafe or ineffective because unit normalization is required before a meaningful measurement comparison; apply Normalize units to an approved canonical scale before joining or aggregating instead.

The action Use a curated Delta table for repeated analytics rather than parsing raw JSON every query solves the same raw sensor payload is available as JSON and Delta; analytics query volume is high because delta tables provide efficient storage structure and reliable write semantics for repeated consumption; it does not by itself solve sensors report temperatures in both Fahrenheit and Celsius, whereas Normalize units to an approved canonical scale before joining or aggregating does.

Although Group by stable machine key and a properly derived event-hour timestamp fits the separate issue that an aggregation must return the maximum temperature per machine and hour because hourly group boundaries should use event time, not string representation or ingest order, this case concerns sensors report temperatures in both Fahrenheit and Celsius and should be resolved with Normalize units to an approved canonical scale before joining or aggregating.

The action Reconcile sink idempotency and recovery semantics before starting with a new checkpoint solves a restart uses a different checkpoint with the same output table because an unrelated checkpoint can cause reprocessing even when the Delta destination already contains prior results; it does not by itself solve an operational stateful aggregation never evicts old windows, whereas Review watermark configuration and maximum tolerated lateness does.

The option Declare exactly-once business outcomes solely because Spark reports the query is running fails to meet an operational stateful aggregation never evicts old windows, given that a running stream alone does not prove duplicate-free target commits; Review watermark configuration and maximum tolerated lateness is the correct scope of action.

Option Use Spark Structured Streaming with a configured trigger and durable checkpoint would focus on the separate issue that a high-volume stream should use micro-batch processing with predictable recovery because a supported micro-batch execution mode combines incremental input and tracked progress, not the current requirement that an operational stateful aggregation never evicts old windows; the appropriate remedy is Review watermark configuration and maximum tolerated lateness.

Although Inspect maintenance job status, scheduling and error results fits the separate issue that a table maintenance operation has been scheduled but optimization has not run because a scheduled task can be queued, skipped or failed even if a UI control is enabled, this case concerns operations wants alerts for failed table maintenance and steadily growing file counts and should be resolved with Monitor maintenance failures and Delta file-count trends with clear thresholds.

Option Compare per-partition source keys and target commit evidence would focus on the separate issue that a nightly notebook succeeds but Delta output has missing expected partitions because spark execution success does not establish complete partition coverage, not the current requirement that operations wants alerts for failed table maintenance and steadily growing file counts; the appropriate remedy is Monitor maintenance failures and Delta file-count trends with clear thresholds.

For this requirement of operations wants alerts for failed table maintenance and steadily growing file counts, Delete all Delta transaction logs to reduce monitoring noise would be unsafe or ineffective because delta log removal would compromise table consistency and recovery; apply Monitor maintenance failures and Delta file-count trends with clear thresholds instead.

Option Deduplicate the staged changes by business key and authoritative source revision would focus on the separate issue that a destination reports duplicate keys during MERGE because a source with multiple versions per key must be resolved before the target merge, not the current requirement that a source system deleted records but the target retains them; the appropriate remedy is Implement delete-aware CDC or reconciliation of tombstones.

For this requirement of a source system deleted records but the target retains them, Move the watermark to tomorrow after a failed run to prevent further errors would be unsafe or ineffective because advancing progress past uncommitted records causes permanent data gaps; apply Implement delete-aware CDC or reconciliation of tombstones instead.

Serialize or safely coordinate dataset-level checkpoint writes is useful when two concurrent pipeline runs overwrite the same progress checkpoint, since racing updates can advance the watermark beyond uncommitted source changes; however, the actual requirement is a source system deleted records but the target retains them, which calls for Implement delete-aware CDC or reconciliation of tombstones.

For this requirement of managers want to know whether optimization actually improved the SLA, Declare an optimization successful solely because it changed a Spark configuration value would be unsafe or ineffective because measured run time and correctness, not configuration changes, determine benefit; apply Benchmark before and after on the same workload using elapsed time, cost and result correctness instead.

Remove global ordering unless it is required for the final business output is useful when the warehouse query spends most of its time performing an unnecessary global sort, since global sorting can force non-scalable stages and large memory use; however, the actual requirement is managers want to know whether optimization actually improved the SLA, which calls for Benchmark before and after on the same workload using elapsed time, cost and result correctness.

The action Inspect Spark shuffle size and reduce unnecessary repartitioning or wide transformations solves the notebook spends most of its time serializing data between partitions because data movement can dominate compute even when transformation expressions are simple; it does not by itself solve managers want to know whether optimization actually improved the SLA, whereas Benchmark before and after on the same workload using elapsed time, cost and result correctness does.