Use FILTER with the appropriate predicate in a table or CALCULATE expression. is an approach for a different requirement: DAX table filtering constructs a subset before the intended aggregation is evaluated. In the present case, time intelligence shifts date filter context to a comparable period rather than subtracting an arbitrary constant.

Use a DAX SUMX iterator over sales rows to sum the row-level product. is an approach for a different requirement: SUMX evaluates an expression per row and then aggregates the results. In the present case, time intelligence shifts date filter context to a comparable period rather than subtracting an arbitrary constant.

Use DISTINCTCOUNT over a stable CustomerKey. is an approach for a different requirement: DISTINCTCOUNT counts unique key values while ordinary row COUNT counts repeated occurrences. In the present case, time intelligence shifts date filter context to a comparable period rather than subtracting an arbitrary constant.

That observation would verify a different goal: A time calculation selection changes both Sales and Margin measures using the same group logic. It does not demonstrate the specific required result: Choosing Units instead of Revenue changes the measure on the same chart.

That observation would verify a different goal: Two connected reports produce the same YTD measure when selecting the same calculation item. It does not demonstrate the specific required result: Choosing Units instead of Revenue changes the measure on the same chart.

That observation would verify a different goal: A named Sales measure responds to the calculation group and retains expected totals. It does not demonstrate the specific required result: Choosing Units instead of Revenue changes the measure on the same chart.

Create a shared Date dimension with appropriate relationships to both facts. is an approach for a different requirement: Conformed dimensions support consistent cross-fact filtering when relationship paths are valid. In the present case, role-playing dates require explicit relationship design when one Date dimension supports multiple date roles.

Align relationship key data types and values before creating the relationship. is an approach for a different requirement: Relationship matching depends on compatible key types and valid key values. In the present case, role-playing dates require explicit relationship design when one Date dimension supports multiple date roles.

Make dimension key values unique and create a one-to-many relationship at the correct grain. is an approach for a different requirement: The unique side of a one-to-many relationship must contain one row per relationship key. In the present case, role-playing dates require explicit relationship design when one Date dimension supports multiple date roles.

That observation would verify a different goal: The same product category filters sales and inventory consistently. It does not demonstrate the specific required result: One selected month filters both orders and refunds to their intended dates.

That observation would verify a different goal: The end-of-month total equals the actual latest balance rather than a sum of daily snapshots. It does not demonstrate the specific required result: One selected month filters both orders and refunds to their intended dates.

That observation would verify a different goal: The corrected relationship creates successfully and a region slicer filters expected facts. It does not demonstrate the specific required result: One selected month filters both orders and refunds to their intended dates.

Use an appropriate inactive relationship with a DAX USERELATIONSHIP calculation for ship-date measures. is an approach for a different requirement: Role-playing dates require explicit relationship design when one Date dimension supports multiple date roles. In the present case, unnecessary bidirectional filters can create ambiguous propagation and unexpected measure results.

Build a bridge mapping table at the customer-territory association grain. is an approach for a different requirement: A bridge makes many-to-many relationships explicit and helps control filter paths. In the present case, unnecessary bidirectional filters can create ambiguous propagation and unexpected measure results.

Make dimension key values unique and create a one-to-many relationship at the correct grain. is an approach for a different requirement: The unique side of a one-to-many relationship must contain one row per relationship key. In the present case, unnecessary bidirectional filters can create ambiguous propagation and unexpected measure results.

That observation would verify a different goal: A refresh captures new source rows and subsequent report queries run from the imported model. It does not demonstrate the specific required result: A new committed source transaction appears in the report without an Import processing cycle.

That observation would verify a different goal: A report combines both sources and updates to the DirectQuery fact are visible promptly. It does not demonstrate the specific required result: A new committed source transaction appears in the report without an Import processing cycle.

That observation would verify a different goal: The model queries the new table successfully after the framing refresh completes. It does not demonstrate the specific required result: A new committed source transaction appears in the report without an Import processing cycle.

That observation would verify a different goal: A restricted test user cannot read protected rows through the Direct Lake on OneLake model. It does not demonstrate the specific required result: The combined model responds correctly to filters spanning the imported lookup and Direct Lake fact.

That observation would verify a different goal: After materialization the affected query uses Direct Lake instead of the slower fallback. It does not demonstrate the specific required result: The combined model responds correctly to filters spanning the imported lookup and Direct Lake fact.

That observation would verify a different goal: Visuals return the expected aggregates while performance traces show the intended Direct Lake path. It does not demonstrate the specific required result: The combined model responds correctly to filters spanning the imported lookup and Direct Lake fact.

That observation would verify a different goal: Paid-only average matches known test rows even in an unfiltered visual. It does not demonstrate the specific required result: The prior-year value matches historical sales for the same calendar period.

That observation would verify a different goal: The highest revenue product receives rank one under the selected period. It does not demonstrate the specific required result: The prior-year value matches historical sales for the same calendar period.

That observation would verify a different goal: Only active history versions contribute to the resulting customer count. It does not demonstrate the specific required result: The prior-year value matches historical sales for the same calendar period.

Use suitable DAX time-intelligence over an appropriate marked Date table. is an approach for a different requirement: Time intelligence shifts date filter context to a comparable period rather than subtracting an arbitrary constant. In the present case, sUMX evaluates an expression per row and then aggregates the results.

Use RANKX or an appropriate DAX window/ranking function over the intended product set. is an approach for a different requirement: A ranking function evaluates measures across the specified comparison table and sort order. In the present case, sUMX evaluates an expression per row and then aggregates the results.

Use a DAX VAR to compute the reusable intermediate result once within the measure. is an approach for a different requirement: Variables improve expression readability and avoid repeated work within the intended evaluation context. In the present case, sUMX evaluates an expression per row and then aggregates the results.

Apply appropriate OneLake or semantic-model security instead of relying on SQL endpoint RLS for that path. is an approach for a different requirement: Direct Lake on OneLake bypasses the SQL analytics endpoint and does not inherit its SQL RLS. In the present case, direct Lake on OneLake executes without SQL endpoint DirectQuery fallback.

Trigger or allow a successful Direct Lake framing refresh after creation or schema changes. is an approach for a different requirement: A framing operation updates references to current Delta files without performing a full Import load. In the present case, direct Lake on OneLake executes without SQL endpoint DirectQuery fallback.

Design a supported composite model combining Import and DirectQuery tables. is an approach for a different requirement: Composite models can mix storage modes when the design and relationships support it. In the present case, direct Lake on OneLake executes without SQL endpoint DirectQuery fallback.

That observation would verify a different goal: Performance traces show the query now executes in Direct Lake rather than falling back. It does not demonstrate the specific required result: Source query traces show an appropriate date predicate instead of a full-table extraction.

That observation would verify a different goal: Restricted users remain protected and query traces show the intended storage behavior. It does not demonstrate the specific required result: Source query traces show an appropriate date predicate instead of a full-table extraction.

That observation would verify a different goal: A data change becomes visible after framing without a full-model row import job. It does not demonstrate the specific required result: Source query traces show an appropriate date predicate instead of a full-table extraction.