Microsoft PL-300 Bundle

That recommendation applies when a Date slicer unexpectedly filters orders through ShipDate rather than OrderDate. This solves “unexpectedly filters orders through ShipDate rather than OrderDate”; the stated issue is “relationship where a conformed dimension could be used”.

A lookup's grain must be one record per relationship key. The deciding difference is “lookup has duplicates so the relationship auto-detects many-to-many” versus “relationship where a conformed dimension could be used”.

Ambiguous paths can create incorrect or prohibited model filtering. That is a different operating condition from “relationship where a conformed dimension could be used”: “between Region and Sales and gets ambiguity errors”.

Calendar drill paths should move from coarser to finer time levels. That is a different operating condition from “Country, State and City for drilldown in visuals”: “a Date hierarchy but puts MonthName above Year”.

Technical relationship keys need not be shown to report consumers. It serves “uses its display attribute”, not “Country, State and City for drilldown in visuals”.

Drilling should resolve from aggregate category to detailed product. That matters for “and must drill from group to individual items” rather than the condition “Country, State and City for drilldown in visuals”.

Drilling should resolve from aggregate category to detailed product. That matters for “and must drill from group to individual items” rather than the condition “a Date hierarchy but puts MonthName above Year”.

An explicit hierarchy defines the intended drill levels. The boundary it addresses is “Country, State and City for drilldown in visuals”, while this case concerns “a Date hierarchy but puts MonthName above Year”.

Technical relationship keys need not be shown to report consumers. This solves “uses its display attribute”; the stated issue is “a Date hierarchy but puts MonthName above Year”.

Calendar drill paths should move from coarser to finer time levels. The deciding difference is “a Date hierarchy but puts MonthName above Year” versus “and must drill from group to individual items”.

An explicit hierarchy defines the intended drill levels. That is a different operating condition from “and must drill from group to individual items”: “Country, State and City for drilldown in visuals”.

Technical relationship keys need not be shown to report consumers. It serves “uses its display attribute”, not “and must drill from group to individual items”.

Calendar drill paths should move from coarser to finer time levels. The present requirement is “groups identical city names in different countries together”; “a Date hierarchy but puts MonthName above Year” is a distinct situation.

An explicit hierarchy defines the intended drill levels. That matters for “Country, State and City for drilldown in visuals” rather than the condition “groups identical city names in different countries together”.

Technical relationship keys need not be shown to report consumers. The boundary it addresses is “uses its display attribute”, while this case concerns “groups identical city names in different countries together”.

An explicit hierarchy defines the intended drill levels. The boundary it addresses is “Country, State and City for drilldown in visuals”, while this case concerns “uses its display attribute”.

Calendar drill paths should move from coarser to finer time levels. This solves “a Date hierarchy but puts MonthName above Year”; the stated issue is “uses its display attribute”.

A city label alone may not identify a unique location. Its application is “groups identical city names in different countries together”, not the need “uses its display attribute”.

Data preparation row multiplication can distort measures even when model relationships are valid. Its application is “Verify merge joins and table grain before loading”, not the need “the dimension is disconnected”.

No matching fact rows may produce blanks even with a valid Date dimension. It serves “outside all fact rows and shows blank periods”, not “the dimension is disconnected”.

Slicer attributes must propagate filters to the measured fact. The present requirement is “the dimension is disconnected”; “filter direction points the wrong way” is a distinct situation.

No matching fact rows may produce blanks even with a valid Date dimension. The present requirement is “filter direction points the wrong way”; “outside all fact rows and shows blank periods” is a distinct situation.

Without a filter path, category rows do not constrain the fact measure. That matters for “the dimension is disconnected” rather than the condition “filter direction points the wrong way”.

Data preparation row multiplication can distort measures even when model relationships are valid. This solves “Verify merge joins and table grain before loading”; the stated issue is “filter direction points the wrong way”.

Slicer attributes must propagate filters to the measured fact. This solves “filter direction points the wrong way”; the stated issue is “outside all fact rows and shows blank periods”.

Data preparation row multiplication can distort measures even when model relationships are valid. Its application is “Verify merge joins and table grain before loading”, not the need “outside all fact rows and shows blank periods”.

Without a filter path, category rows do not constrain the fact measure. That is a different operating condition from “outside all fact rows and shows blank periods”: “the dimension is disconnected”.

Slicer attributes must propagate filters to the measured fact. That is a different operating condition from “table but each key should identify one order”: “filter direction points the wrong way”.

Without a filter path, category rows do not constrain the fact measure. It serves “the dimension is disconnected”, not “table but each key should identify one order”.

No matching fact rows may produce blanks even with a valid Date dimension. That matters for “outside all fact rows and shows blank periods” rather than the condition “table but each key should identify one order”.

Order header as a lookup requires one row per OrderKey. That matters for “table but each key should identify one order” rather than the condition “Verify merge joins and table grain before loading”.

Without a filter path, category rows do not constrain the fact measure. This solves “the dimension is disconnected”; the stated issue is “Verify merge joins and table grain before loading”.

Slicer attributes must propagate filters to the measured fact. The deciding difference is “filter direction points the wrong way” versus “Verify merge joins and table grain before loading”.