Microsoft PL-300 Bundle

Dimension attributes are used for grouping and filtering fact measures. Its application is “Category and Subcategory names but no additive measures”, not the need “though the two are linked by Sales events”.

Transaction facts store measurable events and foreign keys. That is a different operating condition from “though the two are linked by Sales events”: “one row per purchased item, Quantity and NetAmount”.

Keeping high-cardinality text out of facts improves compression and clarity. It serves “Product descriptions on each 100-million-row Sales fact record”, not “though the two are linked by Sales events”.

A one-to-one relationship joins two tables with one matching record per key. The present requirement is “values while Sales repeats those keys many times”; “one row per EmployeeID and carry complementary attributes” is a distinct situation.

The one side requires unique key values. That matters for “the analyst cannot make it the one side” rather than the condition “values while Sales repeats those keys many times”.

Direct fact-to-fact relationships on repeated keys can introduce ambiguous aggregation paths. The boundary it addresses is “fact to a summarized fact using nonunique ProductID”, while this case concerns “values while Sales repeats those keys many times”.

The one side requires unique key values. This solves “the analyst cannot make it the one side”; the stated issue is “Bridge table and Sales contain repeated CustomerSegment IDs”.

Direct fact-to-fact relationships on repeated keys can introduce ambiguous aggregation paths. Its application is “fact to a summarized fact using nonunique ProductID”, not the need “Bridge table and Sales contain repeated CustomerSegment IDs”.

One product can correspond to many fact rows while ProductKey stays unique in the lookup. That is a different operating condition from “Bridge table and Sales contain repeated CustomerSegment IDs”: “values while Sales repeats those keys many times”.

Direct fact-to-fact relationships on repeated keys can introduce ambiguous aggregation paths. That is a different operating condition from “the analyst cannot make it the one side”: “fact to a summarized fact using nonunique ProductID”.

Repeated keys on both sides prevent a simple one-to-many lookup relationship. It serves “Bridge table and Sales contain repeated CustomerSegment IDs”, not “the analyst cannot make it the one side”.

One product can correspond to many fact rows while ProductKey stays unique in the lookup. That matters for “values while Sales repeats those keys many times” rather than the condition “the analyst cannot make it the one side”.

Repeated keys on both sides prevent a simple one-to-many lookup relationship. That matters for “Bridge table and Sales contain repeated CustomerSegment IDs” rather than the condition “one row per EmployeeID and carry complementary attributes”.

One product can correspond to many fact rows while ProductKey stays unique in the lookup. The boundary it addresses is “values while Sales repeats those keys many times”, while this case concerns “one row per EmployeeID and carry complementary attributes”.

The one side requires unique key values. This solves “the analyst cannot make it the one side”; the stated issue is “one row per EmployeeID and carry complementary attributes”.

Repeated keys on both sides prevent a simple one-to-many lookup relationship. Its application is “Bridge table and Sales contain repeated CustomerSegment IDs”, not the need “fact to a summarized fact using nonunique ProductID”.

One product can correspond to many fact rows while ProductKey stays unique in the lookup. That is a different operating condition from “fact to a summarized fact using nonunique ProductID”: “values while Sales repeats those keys many times”.

The one side requires unique key values. It serves “the analyst cannot make it the one side”, not “fact to a summarized fact using nonunique ProductID”.

Many-to-many membership requires careful filter flow and validation. The present requirement is “Sales rows should not filter Product by default”; “and a documented many-to-many design requires controlled propagation” is a distinct situation.

Bidirectional propagation may make filter paths ambiguous and harder to predict. That matters for “create multiple filter paths from Date to Sales” rather than the condition “Sales rows should not filter Product by default”.

Reverse propagation can unexpectedly couple otherwise separate fact tables. The boundary it addresses is “fact filters a dimension and then another fact”, while this case concerns “Sales rows should not filter Product by default”.

CROSSFILTER can modify relationship filter direction within one evaluation. This solves “calculation-specific relationship direction without changing global model behavior”; the stated issue is “create multiple filter paths from Date to Sales”.

Many-to-many membership requires careful filter flow and validation. The deciding difference is “and a documented many-to-many design requires controlled propagation” versus “create multiple filter paths from Date to Sales”.

Reverse propagation can unexpectedly couple otherwise separate fact tables. Its application is “fact filters a dimension and then another fact”, not the need “create multiple filter paths from Date to Sales”.

Reverse propagation can unexpectedly couple otherwise separate fact tables. Its application is “fact filters a dimension and then another fact”, not the need “and a documented many-to-many design requires controlled propagation”.

Single-direction dimension-to-fact filtering is the usual star-schema default. It serves “Sales rows should not filter Product by default”, not “and a documented many-to-many design requires controlled propagation”.

CROSSFILTER can modify relationship filter direction within one evaluation. The present requirement is “and a documented many-to-many design requires controlled propagation”; “calculation-specific relationship direction without changing global model behavior” is a distinct situation.

Bidirectional propagation may make filter paths ambiguous and harder to predict. The present requirement is “calculation-specific relationship direction without changing global model behavior”; “create multiple filter paths from Date to Sales” is a distinct situation.

Reverse propagation can unexpectedly couple otherwise separate fact tables. The boundary it addresses is “fact filters a dimension and then another fact”, while this case concerns “calculation-specific relationship direction without changing global model behavior”.

Many-to-many membership requires careful filter flow and validation. This solves “and a documented many-to-many design requires controlled propagation”; the stated issue is “calculation-specific relationship direction without changing global model behavior”.

Many-to-many membership requires careful filter flow and validation. The deciding difference is “and a documented many-to-many design requires controlled propagation” versus “fact filters a dimension and then another fact”.

Single-direction dimension-to-fact filtering is the usual star-schema default. Its application is “Sales rows should not filter Product by default”, not the need “fact filters a dimension and then another fact”.

Bidirectional propagation may make filter paths ambiguous and harder to predict. That is a different operating condition from “fact filters a dimension and then another fact”: “create multiple filter paths from Date to Sales”.