That observation would verify a different goal: The optimized visual has acceptable latency and preserves the required aggregate results. It does not demonstrate the specific required result: The optimized measure returns the same output and its measured query duration decreases.

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: The optimized measure returns the same output and its measured query duration decreases.

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: The optimized measure returns the same output and its measured query duration decreases.

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 supports composite modeling where its rules permit additional modes.

Use Import mode with an appropriate semantic model refresh schedule. is an approach for a different requirement: Import stores a full local model copy for in-memory querying, trading periodic refresh for interactive performance. In the present case, direct Lake on OneLake supports composite modeling where its rules permit additional modes.

Set DirectLakeBehavior to DirectLakeOnly temporarily during diagnosis where supported. is an approach for a different requirement: DirectLakeOnly fails when conditions for Direct Lake aren't met, rather than silently falling back. In the present case, direct Lake on OneLake supports composite modeling where its rules permit additional modes.

Configure a dynamic format string for the relevant measure or calculation group item. is an approach for a different requirement: Dynamic format strings change presentation format while preserving the underlying numeric value. In the present case, calculation groups apply reusable DAX transformations to many explicit measures.

Define a semantic-model calculation group rather than separate report-specific transformations. is an approach for a different requirement: Centralized calculation items reduce duplicated DAX business logic and support consistency. In the present case, calculation groups apply reusable DAX transformations to many explicit measures.

Use explicit measures and configure the calculation group requirements rather than relying on implicit measures. is an approach for a different requirement: Calculation groups typically require explicit measures because their transformations use selected-measure semantics. In the present case, calculation groups apply reusable DAX transformations to many explicit measures.

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: Performance traces show the query now executes in Direct Lake rather than falling back.

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

That observation would verify a different goal: After maintenance and framing, diagnostics identify the table as executing in Direct Lake. It does not demonstrate the specific required result: Performance traces show the query now executes in Direct Lake rather than falling back.

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.

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, removing the chosen category filter creates a meaningful whole against which to compare each part.

Implement a CALCULATE measure with an explicit Status = Paid filter context. is an approach for a different requirement: CALCULATE applies a specified filter when evaluating the measure. In the present case, removing the chosen category filter creates a meaningful whole against which to compare each part.

Use SELECTEDVALUE with a suitable alternate result for the category context. is an approach for a different requirement: SELECTEDVALUE returns a scalar when exactly one distinct value is available. In the present case, removing the chosen category filter creates a meaningful whole against which to compare each part.

That observation would verify a different goal: The same product category filters sales and inventory consistently. It does not demonstrate the specific required result: The shipment KPI groups by ShipDate while the ordinary sales measure still uses OrderDate.

That observation would verify a different goal: Customers in two territories appear correctly without multiplying unrelated sales totals. It does not demonstrate the specific required result: The shipment KPI groups by ShipDate while the ordinary sales measure still uses OrderDate.

That observation would verify a different goal: Slicing by customer segment returns the correct aggregated sales without duplicate fact rows. It does not demonstrate the specific required result: The shipment KPI groups by ShipDate while the ordinary sales measure still uses OrderDate.

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: An unsupported view produces a clear failure in development and the cause is corrected before production.

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: An unsupported view produces a clear failure in development and the cause is corrected before production.

That observation would verify a different goal: The combined model responds correctly to filters spanning the imported lookup and Direct Lake fact. It does not demonstrate the specific required result: An unsupported view produces a clear failure in development and the cause is corrected before production.

Inspect query folding and move a compatible RangeStart/RangeEnd filter toward the source. is an approach for a different requirement: Query folding can push incremental partition filtering to the data source and reduce transferred data. In the present case, incremental refresh partitions time-based data and reprocesses only the selected recent window.

Use one inclusive and one exclusive time boundary for RangeStart and RangeEnd. is an approach for a different requirement: Nonoverlapping date window predicates avoid the same timestamp belonging to two partitions. In the present case, incremental refresh partitions time-based data and reprocesses only the selected recent window.

Move appropriate access rules to supported semantic-model security where the design permits, or accept fallback. is an approach for a different requirement: SQL endpoint security can force DirectQuery fallback in Direct Lake on SQL; changing layers requires review. In the present case, incremental refresh partitions time-based data and reprocesses only the selected recent window.

Use SELECTEDVALUE with a suitable alternate result for the category context. is an approach for a different requirement: SELECTEDVALUE returns a scalar when exactly one distinct value is available. In the present case, dIVIDE handles zero and blank denominators consistently in DAX measures.

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, dIVIDE handles zero and blank denominators consistently in DAX measures.

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, dIVIDE handles zero and blank denominators consistently in DAX measures.

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.