Microsoft PL-300 Bundle

Production refresh should not depend on one author's workstation. That is a different operating condition from “changes to a pre-existing published model's business measures”: “breaks in the service”.

Shared models reduce duplicated measures and inconsistent business calculations. It serves “model into each report, producing conflicting metric definitions”, not “changes to a pre-existing published model's business measures”.

Standardize common transformations and measures in a certified shared model. The present requirement is “changes to a pre-existing published model's business measures”; “conflicting exchange-rate assumptions to the same executive workspace” is a distinct situation.

Distinct counts all different values, while unique counts values appearing only once. That matters for “unexpected duplicates and the analyst wants distinct-versus-unique counts” rather than the condition “a mixture of numeric values, nulls and errors”.

Detailed profiling reveals extrema, distribution and descriptive statistics. The boundary it addresses is “date column includes anomalous minimum and maximum values”, while this case concerns “a mixture of numeric values, nulls and errors”.

Power Query often profiles a sampled subset unless full-dataset profiling is selected. This solves “in a profile based on only a subset”; the stated issue is “a mixture of numeric values, nulls and errors”.

Frequency counts help identify common repeated values and suspicious data concentrations. This solves “to see which customer IDs appear most frequently”; the stated issue is “unexpected duplicates and the analyst wants distinct-versus-unique counts”.

Column Quality reports the proportions of values by quality status. The deciding difference is “a mixture of numeric values, nulls and errors” versus “unexpected duplicates and the analyst wants distinct-versus-unique counts”.

Power Query often profiles a sampled subset unless full-dataset profiling is selected. Its application is “in a profile based on only a subset”, not the need “unexpected duplicates and the analyst wants distinct-versus-unique counts”.

Distinct counts all different values, while unique counts values appearing only once. That is a different operating condition from “date column includes anomalous minimum and maximum values”: “unexpected duplicates and the analyst wants distinct-versus-unique counts”.

Power Query often profiles a sampled subset unless full-dataset profiling is selected. The present requirement is “date column includes anomalous minimum and maximum values”; “in a profile based on only a subset” is a distinct situation.

Frequency counts help identify common repeated values and suspicious data concentrations. That matters for “to see which customer IDs appear most frequently” rather than the condition “date column includes anomalous minimum and maximum values”.

Distinct counts all different values, while unique counts values appearing only once. That matters for “unexpected duplicates and the analyst wants distinct-versus-unique counts” rather than the condition “in a profile based on only a subset”.

Column Quality reports the proportions of values by quality status. The boundary it addresses is “a mixture of numeric values, nulls and errors”, while this case concerns “in a profile based on only a subset”.

Detailed profiling reveals extrema, distribution and descriptive statistics. The deciding difference is “date column includes anomalous minimum and maximum values” versus “in a profile based on only a subset”.

Column Quality reports the proportions of values by quality status. The deciding difference is “a mixture of numeric values, nulls and errors” versus “to see which customer IDs appear most frequently”.

Detailed profiling reveals extrema, distribution and descriptive statistics. That is a different operating condition from “to see which customer IDs appear most frequently”: “date column includes anomalous minimum and maximum values”.

Distinct counts all different values, while unique counts values appearing only once. It serves “unexpected duplicates and the analyst wants distinct-versus-unique counts”, not “to see which customer IDs appear most frequently”.

Zero may be a valid measurement and must not be conflated with missingness. It serves “but some analysts replace all zeros as missing”, not “must remain distinguishable from true zero sales”.

A specified placeholder can preserve row meaning for optional text values. The present requirement is “must remain distinguishable from true zero sales”; “and approved business rules require a placeholder label” is a distinct situation.

Empty text may not be counted as missing until normalized. That matters for “were imported as text rather than nulls” rather than the condition “must remain distinguishable from true zero sales”.

Empty text may not be counted as missing until normalized. The boundary it addresses is “were imported as text rather than nulls”, while this case concerns “and approved business rules require a placeholder label”.

Replacing unknown revenue with zero can distort aggregate metrics. This solves “must remain distinguishable from true zero sales”; the stated issue is “and approved business rules require a placeholder label”.

Quarantine or reject rows lacking mandatory relationship keys. Its application is “and cannot be safely joined to a customer”, not the need “and approved business rules require a placeholder label”.

A specified placeholder can preserve row meaning for optional text values. That is a different operating condition from “were imported as text rather than nulls”: “and approved business rules require a placeholder label”.

Zero may be a valid measurement and must not be conflated with missingness. It serves “but some analysts replace all zeros as missing”, not “were imported as text rather than nulls”.

Replacing unknown revenue with zero can distort aggregate metrics. The present requirement is “were imported as text rather than nulls”; “must remain distinguishable from true zero sales” is a distinct situation.

Replacing unknown revenue with zero can distort aggregate metrics. That matters for “must remain distinguishable from true zero sales” rather than the condition “and cannot be safely joined to a customer”.

Empty text may not be counted as missing until normalized. The boundary it addresses is “were imported as text rather than nulls”, while this case concerns “and cannot be safely joined to a customer”.

A specified placeholder can preserve row meaning for optional text values. This solves “and approved business rules require a placeholder label”; the stated issue is “and cannot be safely joined to a customer”.

A specified placeholder can preserve row meaning for optional text values. This solves “and approved business rules require a placeholder label”; the stated issue is “but some analysts replace all zeros as missing”.

Quarantine or reject rows lacking mandatory relationship keys. The deciding difference is “and cannot be safely joined to a customer” versus “but some analysts replace all zeros as missing”.

Replacing unknown revenue with zero can distort aggregate metrics. Its application is “must remain distinguishable from true zero sales”, not the need “but some analysts replace all zeros as missing”.