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File layout changes and targeted query scan cost decreases would demonstrate the separate action Run supported `OPTIMIZE` or automatic clustering maintenance, which addresses “A large table is already liquid clustered on a new key but old files are not reorganized.” It does not verify the requirement “A data engineer proposes partitioning by a unique transaction identifier” following Reject the high-cardinality partition plan and choose clustering or a better low-cardinality layout.

This signal belongs to the adjacent case “Queries commonly filter on different high-cardinality attributes that change over time,” whose remedy is Use supported liquid clustering rather than creating thousands of tiny partitions. The observed result for the present requirement is The table avoids one-file-per-key patterns and maintains manageable file count.

File count and average file size improve in that table pertains to a different control (Execute a scoped OPTIMIZE maintenance job during a suitable window) and a different need (One unusually fragmented table needs a controlled immediate compaction). It cannot establish that Reject the high-cardinality partition plan and choose clustering or a better low-cardinality layout met the stated goal.

Create a Unity Catalog managed Delta table with eligible predictive optimization is appropriate when “The team wants Databricks to manage table data lifecycle and optimization automatically”; it would be verified by Maintenance history shows supported automatic optimization tasks for the managed table. It does not directly satisfy “Optimizations must run automatically for eligible managed Delta tables,” whose relevant acceptance evidence is Maintenance history shows automatic optimization on eligible tables.

Use `CLUSTER BY AUTO` with required predictive optimization support belongs to the separate case “A UC managed table should choose clustering columns from observed workloads,” where Selected keys adapt to query patterns over time where beneficial would be expected. That differs from the condition in this stem: “Optimizations must run automatically for eligible managed Delta tables.”

The alternative Use cost-aware optimization policies rather than blanket frequent rewrites has a distinct purpose—Maintenance cost is rising on tables rarely queried. It would not establish the required outcome (Optimizations must run automatically for eligible managed Delta tables); the necessary verification concerns Maintenance history shows automatic optimization on eligible tables.

Optimization activity and improved data skipping follow the workload would demonstrate the separate action Enable appropriate predictive optimization features for supported managed tables, which addresses “A managed table's storage optimization should reflect observed query patterns automatically.” It does not verify the requirement “Optimizations must run automatically for eligible managed Delta tables” following Enable predictive optimization with supported workspace/metastore settings.

That observation is a valid indicator for “A UC managed table should choose clustering columns from observed workloads” when using Use `CLUSTER BY AUTO` with required predictive optimization support. The scenario instead tests “Optimizations must run automatically for eligible managed Delta tables”; the relevant result is Maintenance history shows automatic optimization on eligible tables.

This signal belongs to the adjacent case “One unusually fragmented table needs a controlled immediate compaction,” whose remedy is Execute a scoped OPTIMIZE maintenance job during a suitable window. The observed result for the present requirement is Maintenance history shows automatic optimization on eligible tables.

This action solves a different issue: “A table needs to preserve transaction correctness during physical file compaction.” Its normal signal is Reads see consistent table versions before and after compaction. Here the requested behavior is “One unusually fragmented table needs a controlled immediate compaction,” so that signal is not sufficient.

Set retention policy based on documented recovery requirements before cleanup is appropriate when “The table's retention setting conflicts with consumers needing older time travel snapshots”; it would be verified by Required historical versions remain queryable throughout retention window. It does not directly satisfy “One unusually fragmented table needs a controlled immediate compaction,” whose relevant acceptance evidence is File count and average file size improve in that table.

The alternative Use liquid clustering or suitable layout instead of one partition per key has a distinct purpose—A high-cardinality filter benefits from clustering rather than massive Hive partitions. It would not establish the required outcome (One unusually fragmented table needs a controlled immediate compaction); the necessary verification concerns File count and average file size improve in that table.

Maintenance history shows automatic optimization on eligible tables pertains to a different control (Enable predictive optimization with supported workspace/metastore settings) and a different need (Optimizations must run automatically for eligible managed Delta tables). It cannot establish that Execute a scoped OPTIMIZE maintenance job during a suitable window met the stated goal.

This signal belongs to the adjacent case “A table needs to preserve transaction correctness during physical file compaction,” whose remedy is Use supported Delta OPTIMIZE rather than directly rewriting the data directory. The observed result for the present requirement is File count and average file size improve in that table.

The table avoids one-file-per-key patterns and maintains manageable file count would demonstrate the separate action Reject the high-cardinality partition plan and choose clustering or a better low-cardinality layout, which addresses “A data engineer proposes partitioning by a unique transaction identifier.” It does not verify the requirement “One unusually fragmented table needs a controlled immediate compaction” following Execute a scoped OPTIMIZE maintenance job during a suitable window.

Use cost-aware optimization policies rather than blanket frequent rewrites is appropriate when “Maintenance cost is rising on tables rarely queried”; it would be verified by Automatic/manual maintenance targets tables with demonstrated query benefit. It does not directly satisfy “Table statistics are stale after a large load and the planner misestimates joins,” whose relevant acceptance evidence is Query plan estimates and join selection improve on benchmark input.

Run supported `OPTIMIZE` or automatic clustering maintenance belongs to the separate case “A large table is already liquid clustered on a new key but old files are not reorganized,” where File layout changes and targeted query scan cost decreases would be expected. That differs from the condition in this stem: “Table statistics are stale after a large load and the planner misestimates joins.”

The alternative Use eligible Photon-enabled compute for the workload has a distinct purpose—Large SQL analytic scans are CPU-bound under normal Spark engine execution. It would not establish the required outcome (Table statistics are stale after a large load and the planner misestimates joins); the necessary verification concerns Query plan estimates and join selection improve on benchmark input.

Automatic/manual maintenance targets tables with demonstrated query benefit pertains to a different control (Use cost-aware optimization policies rather than blanket frequent rewrites) and a different need (Maintenance cost is rising on tables rarely queried). It cannot establish that Refresh relevant supported statistics or optimizer metadata met the stated goal.

That observation is a valid indicator for “The planner initially picks a shuffle join but one input becomes broadcast-eligible after filtering” when using Allow AQE to adapt supported join strategy at runtime. The scenario instead tests “Table statistics are stale after a large load and the planner misestimates joins”; the relevant result is Query plan estimates and join selection improve on benchmark input.

Benchmark reports lower CPU time or execution latency with same results pertains to a different control (Use eligible Photon-enabled compute for the workload) and a different need (Large SQL analytic scans are CPU-bound under normal Spark engine execution). It cannot establish that Refresh relevant supported statistics or optimizer metadata met the stated goal.

The alternative Use appropriately configured `VACUUM` with safe retention conditions has a distinct purpose—The table has huge amounts of obsolete files and operators need retention-aware cleanup. It would not establish the required outcome (The table's retention setting conflicts with consumers needing older time travel snapshots); the necessary verification concerns Required historical versions remain queryable throughout retention window.

This action solves a different issue: “A team enables acceleration but sees no benefit because source data scanning dominates.” Its normal signal is The bottleneck is attributed to read volume rather than engine computation. Here the requested behavior is “The table's retention setting conflicts with consumers needing older time travel snapshots,” so that signal is not sufficient.

Refresh relevant supported statistics or optimizer metadata is appropriate when “Table statistics are stale after a large load and the planner misestimates joins”; it would be verified by Query plan estimates and join selection improve on benchmark input. It does not directly satisfy “The table's retention setting conflicts with consumers needing older time travel snapshots,” whose relevant acceptance evidence is Required historical versions remain queryable throughout retention window.

That observation is a valid indicator for “The table has huge amounts of obsolete files and operators need retention-aware cleanup” when using Use appropriately configured `VACUUM` with safe retention conditions. The scenario instead tests “The table's retention setting conflicts with consumers needing older time travel snapshots”; the relevant result is Required historical versions remain queryable throughout retention window.

Query plan estimates and join selection improve on benchmark input pertains to a different control (Refresh relevant supported statistics or optimizer metadata) and a different need (Table statistics are stale after a large load and the planner misestimates joins). It cannot establish that Set retention policy based on documented recovery requirements before cleanup met the stated goal.

That observation is a valid indicator for “An important dataset must remain queryable even after cached worker nodes terminate” when using Store the authoritative table in governed cloud object storage, not cache. The scenario instead tests “The table's retention setting conflicts with consumers needing older time travel snapshots”; the relevant result is Required historical versions remain queryable throughout retention window.

Accept cost-aware optimizer selection rather than force arbitrary keys belongs to the separate case “A very small table receives no automatic clustering keys despite configuration,” where Optimizer avoids unnecessary rewrites when predicted benefit is negligible would be expected. That differs from the condition in this stem: “Maintenance cost is rising on tables rarely queried.”

This action solves a different issue: “A high-cardinality filter benefits from clustering rather than massive Hive partitions.” Its normal signal is File count and selective scan cost decrease without partition explosion. Here the requested behavior is “Maintenance cost is rising on tables rarely queried,” so that signal is not sufficient.

Refresh relevant supported statistics or optimizer metadata belongs to the separate case “Table statistics are stale after a large load and the planner misestimates joins,” where Query plan estimates and join selection improve on benchmark input would be expected. That differs from the condition in this stem: “Maintenance cost is rising on tables rarely queried.”

This signal belongs to the adjacent case “A very small table receives no automatic clustering keys despite configuration,” whose remedy is Accept cost-aware optimizer selection rather than force arbitrary keys. The observed result for the present requirement is Automatic/manual maintenance targets tables with demonstrated query benefit.

That observation is a valid indicator for “The team wants Databricks to manage table data lifecycle and optimization automatically” when using Create a Unity Catalog managed Delta table with eligible predictive optimization. The scenario instead tests “Maintenance cost is rising on tables rarely queried”; the relevant result is Automatic/manual maintenance targets tables with demonstrated query benefit.

File count and average file size improve in that table pertains to a different control (Execute a scoped OPTIMIZE maintenance job during a suitable window) and a different need (One unusually fragmented table needs a controlled immediate compaction). It cannot establish that Use cost-aware optimization policies rather than blanket frequent rewrites met the stated goal.