Exam Bundle

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: Remove unneeded source columns before loading into the model.

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: Remove unneeded source columns before loading into the model.

The generated query returns manageable rows within the performance threshold. That check would be meaningful for the separate case: A DirectQuery report times out because a visual requests detailed customer records without filtering. It would not directly verify this scenario's selected implementation: Remove unneeded source columns before loading into the model.

Using 'Apply a suitable Top N filter and provide a separate detail path' supports the result 'Visual rendering time improves while the top categories remain correct'. It does not implement 'Record visual timings with Performance Analyzer and inspect the slow component' or prove 'The investigation identifies whether DAX query time or visual display dominates the delay' for this question.

'Review cross-filter directions and prefer deterministic dimension-to-fact paths' is validated when 'Queries use simpler relationship paths and return the same intended filtered results'. It does not demonstrate 'The investigation identifies whether DAX query time or visual display dominates the delay', which requires 'Record visual timings with Performance Analyzer and inspect the slow component'.

Copying a visual's DAX query enables isolated query analysis; it does not determine whether the original report delay is due to query execution or visual rendering. Performance Analyzer timings must distinguish those components first.

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: Record visual timings with Performance Analyzer and inspect the slow component.

Queries use simpler relationship paths and return the same intended filtered results. That check would be meaningful for the separate case: An analyst sees slow queries from two active bidirectional relationships with ambiguous propagation. It would not directly verify this scenario's selected implementation: Record visual timings with Performance Analyzer and inspect the slow component.

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: Record visual timings with Performance Analyzer and inspect the slow component.

The check for 'Disable or remove them from load after ensuring transformations still work' is 'The model excludes those columns and all dependent calculations remain valid'. That does not establish 'The optimized measure returns the same totals with lower DAX execution duration'; the stated requirement calls for 'Simplify filter expressions and avoid unnecessary whole-fact iteration where equivalent'.

'Eliminate the unnecessary duplicate or derive only needed aggregated fields' addresses the mechanism measured by 'Memory use falls while original fact-based measures still reconcile'. The current outcome is 'The optimized measure returns the same totals with lower DAX execution duration' after 'Simplify filter expressions and avoid unnecessary whole-fact iteration where equivalent', which the distractor leaves unverified.

'Model statistics show reduced memory footprint without affecting supported report fields' would demonstrate 'Drop unused high-cardinality text columns to reduce model memory pressure', but not 'The optimized measure returns the same totals with lower DAX execution duration'. The latter requires 'Simplify filter expressions and avoid unnecessary whole-fact iteration where equivalent' in this scenario.

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: Simplify filter expressions and avoid unnecessary whole-fact iteration where equivalent.

Model statistics show reduced memory footprint without affecting supported report fields. That check would be meaningful for the separate case: A table has both long free-text support notes and a large UUID-like record column not required in visuals. It would not directly verify this scenario's selected implementation: Simplify filter expressions and avoid unnecessary whole-fact iteration where equivalent.

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: Simplify filter expressions and avoid unnecessary whole-fact iteration where equivalent.

Using 'Reuse well-defined base measures and variables where suitable' supports the result 'Query timings improve while numeric results are identical on test selections'. It does not implement 'Build a suitable aggregated fact at product-day grain, preserving detail separately if necessary' or prove 'Summary queries scan fewer rows and reconcile to the detailed source totals' for this question.

'Reconsider conformed dimensions and fact grain rather than defaulting to a direct many-to-many relationship' is validated when 'A refactored dimension model preserves totals and improves query execution stability'. It does not demonstrate 'Summary queries scan fewer rows and reconcile to the detailed source totals', which requires 'Build a suitable aggregated fact at product-day grain, preserving detail separately if necessary'.

A successful 'Use DAX variables to cache reusable intermediate results where semantics permit' would show 'Performance Analyzer demonstrates a reduced DAX time with unchanged totals'. This case needs 'Build a suitable aggregated fact at product-day grain, preserving detail separately if necessary' and evidence that 'Summary queries scan fewer rows and reconcile to the detailed source totals', not the other outcome.

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: Build a suitable aggregated fact at product-day grain, preserving detail separately if necessary.

Query timings improve while numeric results are identical on test selections. That check would be meaningful for the separate case: A report runs several identical expensive DAX expressions in each measure. It would not directly verify this scenario's selected implementation: Build a suitable aggregated fact at product-day grain, preserving detail separately if necessary.

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: Build a suitable aggregated fact at product-day grain, preserving detail separately if necessary.

The check for 'Reduce visual granularity and use appropriate source-side filtering or aggregation' is 'The generated query returns manageable rows within the performance threshold'. That does not establish 'Queries use simpler relationship paths and return the same intended filtered results'; the stated requirement calls for 'Review cross-filter directions and prefer deterministic dimension-to-fact paths'.

'Move suitable static transformations to the preparation layer when practical' addresses the mechanism measured by 'The model's calculated-column cost decreases without changing the intended values'. The current outcome is 'Queries use simpler relationship paths and return the same intended filtered results' after 'Review cross-filter directions and prefer deterministic dimension-to-fact paths', which the distractor leaves unverified.

'The new model answers required monthly comparisons with fewer stored rows' would demonstrate 'Reduce modeling granularity to monthly product aggregates after confirming no daily drill requirement', but not 'Queries use simpler relationship paths and return the same intended filtered results'. The latter requires 'Review cross-filter directions and prefer deterministic dimension-to-fact paths' in this scenario.

The generated query returns manageable rows within the performance threshold. That check would be meaningful for the separate case: A DirectQuery report times out because a visual requests detailed customer records without filtering. It would not directly verify this scenario's selected implementation: Review cross-filter directions and prefer deterministic dimension-to-fact paths.

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: Review cross-filter directions and prefer deterministic dimension-to-fact paths.

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: Review cross-filter directions and prefer deterministic dimension-to-fact paths.

Using 'Apply a suitable Top N filter and provide a separate detail path' supports the result 'Visual rendering time improves while the top categories remain correct'. It does not implement 'Drop unused high-cardinality text columns to reduce model memory pressure' or prove 'Model statistics show reduced memory footprint without affecting supported report fields' 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 'Model statistics show reduced memory footprint without affecting supported report fields', which requires 'Drop unused high-cardinality text columns to reduce model memory pressure'.

A successful 'Review whether disconnected parameter tables are necessary and consolidate redundant ones' would show 'The semantic model has fewer unnecessary tables and parameter interactions continue to work'. This case needs 'Drop unused high-cardinality text columns to reduce model memory pressure' and evidence that 'Model statistics show reduced memory footprint without affecting supported report fields', not the other outcome.

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.