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Measured bottlenecks identify which visual or interaction should be optimized first. That check would be meaningful for the separate case: A report uses several visuals with complex conditional formatting, all refreshing on every selection. It would not directly verify this scenario's selected implementation: Drop unused high-cardinality text columns to reduce model memory pressure.

Visual rendering time improves while the top categories remain correct. That check would be meaningful for the separate case: A dashboard needs a top-N category comparison but displays all ten thousand categories in one chart. It would not directly verify this scenario's selected implementation: Drop unused high-cardinality text columns to reduce model memory pressure.

The semantic model has fewer unnecessary tables and parameter interactions continue to work. That check would be meaningful for the separate case: A model contains 50 separate calculated tables used only as parameter lists with no filtering relationships. It would not directly verify this scenario's selected implementation: Drop unused high-cardinality text columns to reduce model memory pressure.

The check for 'Copy the visual's query from Performance Analyzer to DAX query view for inspection' is 'The copied query reproduces the visual result and can be timed independently'. That does not establish 'Query timings improve while numeric results are identical on test selections'; the stated requirement calls for 'Reuse well-defined base measures and variables where suitable'.

'Benchmark supported storage modes against refresh, query latency and source requirements' addresses the mechanism measured by 'A repeatable performance record shows the chosen mode meets freshness and latency goals'. The current outcome is 'Query timings improve while numeric results are identical on test selections' after 'Reuse well-defined base measures and variables where suitable', which the distractor leaves unverified.

'Memory use falls while original fact-based measures still reconcile' would demonstrate 'Eliminate the unnecessary duplicate or derive only needed aggregated fields', but not 'Query timings improve while numeric results are identical on test selections'. The latter requires 'Reuse well-defined base measures and variables where suitable' in this scenario.

A repeatable performance record shows the chosen mode meets freshness and latency goals. That check would be meaningful for the separate case: An analyst wants to compare Import, DirectQuery and Direct Lake performance for a defined workload. It would not directly verify this scenario's selected implementation: Reuse well-defined base measures and variables where suitable.

The copied query reproduces the visual result and can be timed independently. That check would be meaningful for the separate case: An analyst needs to assess the exact DAX generated for a slow report visual. It would not directly verify this scenario's selected implementation: Reuse well-defined base measures and variables where suitable.

Memory use falls while original fact-based measures still reconcile. That check would be meaningful for the separate case: A calculated table materializes a broad text-heavy duplicate of a fact table that changes rarely. It would not directly verify this scenario's selected implementation: Reuse well-defined base measures and variables where suitable.

Using 'Disable or remove them from load after ensuring transformations still work' supports the result 'The model excludes those columns and all dependent calculations remain valid'. It does not implement 'Reduce visual granularity and use appropriate source-side filtering or aggregation' or prove 'The generated query returns manageable rows within the performance threshold' for this question.

'Use DAX variables to cache reusable intermediate results where semantics permit' is validated when 'Performance Analyzer demonstrates a reduced DAX time with unchanged totals'. It does not demonstrate 'The generated query returns manageable rows within the performance threshold', which requires 'Reduce visual granularity and use appropriate source-side filtering or aggregation'.

A successful 'Remove unneeded source columns before loading into the model' would show 'Model size decreases while required report measures remain unchanged'. This case needs 'Reduce visual granularity and use appropriate source-side filtering or aggregation' and evidence that 'The generated query returns manageable rows within the performance threshold', not the other outcome.

Model size decreases while required report measures remain unchanged. That check would be meaningful for the separate case: A report imports 30 unused text fields with large narrative payloads into a 20-million-row fact. It would not directly verify this scenario's selected implementation: Reduce visual granularity and use appropriate source-side filtering or aggregation.

The model excludes those columns and all dependent calculations remain valid. That check would be meaningful for the separate case: A report contains unused columns with thousands of distinct text values used only during ETL. It would not directly verify this scenario's selected implementation: Reduce visual granularity and use appropriate source-side filtering or aggregation.

Performance Analyzer demonstrates a reduced DAX time with unchanged totals. That check would be meaningful for the separate case: An author suspects that a complex measure recomputes the same intermediate table repeatedly. It would not directly verify this scenario's selected implementation: Reduce visual granularity and use appropriate source-side filtering or aggregation.

The check for 'Record visual timings with Performance Analyzer and inspect the slow component' is 'The investigation identifies whether DAX query time or visual display dominates the delay'. That does not establish 'Visual rendering time improves while the top categories remain correct'; the stated requirement calls for 'Apply a suitable Top N filter and provide a separate detail path'.

'Reduce modeling granularity to monthly product aggregates after confirming no daily drill requirement' addresses the mechanism measured by 'The new model answers required monthly comparisons with fewer stored rows'. The current outcome is 'Visual rendering time improves while the top categories remain correct' after 'Apply a suitable Top N filter and provide a separate detail path', which the distractor leaves unverified.

'A refactored dimension model preserves totals and improves query execution stability' would demonstrate 'Reconsider conformed dimensions and fact grain rather than defaulting to a direct many-to-many relationship', but not 'Visual rendering time improves while the top categories remain correct'. The latter requires 'Apply a suitable Top N filter and provide a separate detail path' in this scenario.

The investigation identifies whether DAX query time or visual display dominates the delay. That check would be meaningful for the separate case: A report's main visual takes 20 seconds, and the analyst cannot tell whether DAX or rendering is responsible. It would not directly verify this scenario's selected implementation: Apply a suitable Top N filter and provide a separate detail path.

The new model answers required monthly comparisons with fewer stored rows. That check would be meaningful for the separate case: A daily-level imported fact has 100 million rows but users only compare monthly product sales. It would not directly verify this scenario's selected implementation: Apply a suitable Top N filter and provide a separate detail path.

A refactored dimension model preserves totals and improves query execution stability. That check would be meaningful for the separate case: A relationship ties two high-cardinality fact tables many-to-many and produces expensive limited relationships. It would not directly verify this scenario's selected implementation: Apply a suitable Top N filter and provide a separate detail path.

Using 'Move suitable static transformations to the preparation layer when practical' supports the result 'The model's calculated-column cost decreases without changing the intended values'. It does not implement 'Eliminate the unnecessary duplicate or derive only needed aggregated fields' or prove 'Memory use falls while original fact-based measures still reconcile' for this question.

'Capture each visual's rendering and query time before removing interactions' is validated when 'Measured bottlenecks identify which visual or interaction should be optimized first'. It does not demonstrate 'Memory use falls while original fact-based measures still reconcile', which requires 'Eliminate the unnecessary duplicate or derive only needed aggregated fields'.

A successful 'Simplify filter expressions and avoid unnecessary whole-fact iteration where equivalent' would show 'The optimized measure returns the same totals with lower DAX execution duration'. This case needs 'Eliminate the unnecessary duplicate or derive only needed aggregated fields' and evidence that 'Memory use falls while original fact-based measures still reconcile', not the other outcome.

The model's calculated-column cost decreases without changing the intended values. That check would be meaningful for the separate case: A dashboard repeats calculated columns that could be computed once in Power Query from static row attributes. It would not directly verify this scenario's selected implementation: Eliminate the unnecessary duplicate or derive only needed aggregated fields.

Measured bottlenecks identify which visual or interaction should be optimized first. That check would be meaningful for the separate case: A report uses several visuals with complex conditional formatting, all refreshing on every selection. It would not directly verify this scenario's selected implementation: Eliminate the unnecessary duplicate or derive only needed aggregated fields.

The optimized measure returns the same totals with lower DAX execution duration. That check would be meaningful for the separate case: A measure uses FILTER(ALL(Sales),...) over a huge fact when a simple dimension filter would suffice. It would not directly verify this scenario's selected implementation: Eliminate the unnecessary duplicate or derive only needed aggregated fields.

The check for 'Build a suitable aggregated fact at product-day grain, preserving detail separately if necessary' is 'Summary queries scan fewer rows and reconcile to the detailed source totals'. That does not establish 'Performance Analyzer demonstrates a reduced DAX time with unchanged totals'; the stated requirement calls for 'Use DAX variables to cache reusable intermediate results where semantics permit'.

'Copy the visual's query from Performance Analyzer to DAX query view for inspection' addresses the mechanism measured by 'The copied query reproduces the visual result and can be timed independently'. The current outcome is 'Performance Analyzer demonstrates a reduced DAX time with unchanged totals' after 'Use DAX variables to cache reusable intermediate results where semantics permit', which the distractor leaves unverified.

'The semantic model has fewer unnecessary tables and parameter interactions continue to work' would demonstrate 'Review whether disconnected parameter tables are necessary and consolidate redundant ones', but not 'Performance Analyzer demonstrates a reduced DAX time with unchanged totals'. The latter requires 'Use DAX variables to cache reusable intermediate results where semantics permit' in this scenario.

Summary queries scan fewer rows and reconcile to the detailed source totals. That check would be meaningful for the separate case: A model has a detailed transaction fact but the dashboard only needs daily summary by product. It would not directly verify this scenario's selected implementation: Use DAX variables to cache reusable intermediate results where semantics permit.

The semantic model has fewer unnecessary tables and parameter interactions continue to work. That check would be meaningful for the separate case: A model contains 50 separate calculated tables used only as parameter lists with no filtering relationships. It would not directly verify this scenario's selected implementation: Use DAX variables to cache reusable intermediate results where semantics permit.

The copied query reproduces the visual result and can be timed independently. That check would be meaningful for the separate case: An analyst needs to assess the exact DAX generated for a slow report visual. It would not directly verify this scenario's selected implementation: Use DAX variables to cache reusable intermediate results where semantics permit.