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This signal belongs to the adjacent case “A raw events dataset must remove exact duplicate records without altering other fields,” whose remedy is Apply a full-record DISTINCT projection to the intended columns. The observed result for the present requirement is Valid optional-null rows pass while null mandatory fields are flagged.

That observation is a valid indicator for “A full snapshot source is authoritative and the target should delete keys absent from it” when using Use a carefully scoped `WHEN NOT MATCHED BY SOURCE THEN DELETE` clause. The scenario instead tests “An existing expectation is too broad and flags legitimate nulls in an optional attribute”; the relevant result is Valid optional-null rows pass while null mandatory fields are flagged.

Quarantine contains the rejected records and silver contains only valid records pertains to a different control (Write a separate selection of failing rows to a quarantine target while the clean flow filters them) and a different need (Bad records must be preserved in a separate quarantine dataset). It cannot establish that Narrow the expectation predicate to the required mandatory fields met the stated goal.

Use a LEFT JOIN from customers to transaction aggregates belongs to the separate case “Customers with no transactions must remain in a customer enrichment result,” where The output includes customers with null transaction values would be expected. That differs from the condition in this stem: “Per-customer latest event is needed while retaining the full winning row.”

Apply a full-record DISTINCT projection to the intended columns belongs to the separate case “A raw events dataset must remove exact duplicate records without altering other fields,” where Identical duplicate rows collapse to one while distinct rows stay would be expected. That differs from the condition in this stem: “Per-customer latest event is needed while retaining the full winning row.”

The alternative Use `GROUP BY product_id, sale_date` with `SUM(amount)` has a distinct purpose—Sales totals should be calculated by product and calendar date. It would not establish the required outcome (Per-customer latest event is needed while retaining the full winning row); the necessary verification concerns Only the highest-ordered event row per customer is selected.

The output includes customers with null transaction values pertains to a different control (Use a LEFT JOIN from customers to transaction aggregates) and a different need (Customers with no transactions must remain in a customer enrichment result). It cannot establish that Use `ROW_NUMBER()` with `PARTITION BY customer_id` and deterministic order met the stated goal.

That observation is a valid indicator for “A raw events dataset must remove exact duplicate records without altering other fields” when using Apply a full-record DISTINCT projection to the intended columns. The scenario instead tests “Per-customer latest event is needed while retaining the full winning row”; the relevant result is Only the highest-ordered event row per customer is selected.

That observation is a valid indicator for “Records need to be ranked within each region without collapsing individual rows” when using Use a ranking window with `PARTITION BY region`. The scenario instead tests “Per-customer latest event is needed while retaining the full winning row”; the relevant result is Only the highest-ordered event row per customer is selected.

The alternative Use a LEFT JOIN from customers to transaction aggregates has a distinct purpose—Customers with no transactions must remain in a customer enrichment result. It would not establish the required outcome (Sales totals should be calculated by product and calendar date); the necessary verification concerns One aggregate row appears for each product/date pair.

Apply a full-record DISTINCT projection to the intended columns belongs to the separate case “A raw events dataset must remove exact duplicate records without altering other fields,” where Identical duplicate rows collapse to one while distinct rows stay would be expected. That differs from the condition in this stem: “Sales totals should be calculated by product and calendar date.”

The alternative Use a ranking window with `PARTITION BY region` has a distinct purpose—Records need to be ranked within each region without collapsing individual rows. It would not establish the required outcome (Sales totals should be calculated by product and calendar date); the necessary verification concerns One aggregate row appears for each product/date pair.

Each input record retains a rank within its region pertains to a different control (Use a ranking window with `PARTITION BY region`) and a different need (Records need to be ranked within each region without collapsing individual rows). It cannot establish that Use `GROUP BY product_id, sale_date` with `SUM(amount)` met the stated goal.

That observation is a valid indicator for “A raw events dataset must remove exact duplicate records without altering other fields” when using Apply a full-record DISTINCT projection to the intended columns. The scenario instead tests “Sales totals should be calculated by product and calendar date”; the relevant result is One aggregate row appears for each product/date pair.

That observation is a valid indicator for “Customers with no transactions must remain in a customer enrichment result” when using Use a LEFT JOIN from customers to transaction aggregates. The scenario instead tests “Sales totals should be calculated by product and calendar date”; the relevant result is One aggregate row appears for each product/date pair.

This action solves a different issue: “A raw events dataset must remove exact duplicate records without altering other fields.” Its normal signal is Identical duplicate rows collapse to one while distinct rows stay. Here the requested behavior is “Records need to be ranked within each region without collapsing individual rows,” so that signal is not sufficient.

Use `GROUP BY product_id, sale_date` with `SUM(amount)` belongs to the separate case “Sales totals should be calculated by product and calendar date,” where One aggregate row appears for each product/date pair would be expected. That differs from the condition in this stem: “Records need to be ranked within each region without collapsing individual rows.”

The alternative Use a LEFT JOIN from customers to transaction aggregates has a distinct purpose—Customers with no transactions must remain in a customer enrichment result. It would not establish the required outcome (Records need to be ranked within each region without collapsing individual rows); the necessary verification concerns Each input record retains a rank within its region.

That observation is a valid indicator for “Sales totals should be calculated by product and calendar date” when using Use `GROUP BY product_id, sale_date` with `SUM(amount)`. The scenario instead tests “Records need to be ranked within each region without collapsing individual rows”; the relevant result is Each input record retains a rank within its region.

Only the highest-ordered event row per customer is selected pertains to a different control (Use `ROW_NUMBER()` with `PARTITION BY customer_id` and deterministic order) and a different need (Per-customer latest event is needed while retaining the full winning row). It cannot establish that Use a ranking window with `PARTITION BY region` met the stated goal.

Identical duplicate rows collapse to one while distinct rows stay would demonstrate the separate action Apply a full-record DISTINCT projection to the intended columns, which addresses “A raw events dataset must remove exact duplicate records without altering other fields.” It does not verify the requirement “Records need to be ranked within each region without collapsing individual rows” following Use a ranking window with `PARTITION BY region`.

This action solves a different issue: “Sales totals should be calculated by product and calendar date.” Its normal signal is One aggregate row appears for each product/date pair. Here the requested behavior is “Customers with no transactions must remain in a customer enrichment result,” so that signal is not sufficient.

Apply a full-record DISTINCT projection to the intended columns is appropriate when “A raw events dataset must remove exact duplicate records without altering other fields”; it would be verified by Identical duplicate rows collapse to one while distinct rows stay. It does not directly satisfy “Customers with no transactions must remain in a customer enrichment result,” whose relevant acceptance evidence is The output includes customers with null transaction values.

Use a ranking window with `PARTITION BY region` belongs to the separate case “Records need to be ranked within each region without collapsing individual rows,” where Each input record retains a rank within its region would be expected. That differs from the condition in this stem: “Customers with no transactions must remain in a customer enrichment result.”

That observation is a valid indicator for “Per-customer latest event is needed while retaining the full winning row” when using Use `ROW_NUMBER()` with `PARTITION BY customer_id` and deterministic order. The scenario instead tests “Customers with no transactions must remain in a customer enrichment result”; the relevant result is The output includes customers with null transaction values.

This signal belongs to the adjacent case “Sales totals should be calculated by product and calendar date,” whose remedy is Use `GROUP BY product_id, sale_date` with `SUM(amount)`. The observed result for the present requirement is The output includes customers with null transaction values.

Identical duplicate rows collapse to one while distinct rows stay pertains to a different control (Apply a full-record DISTINCT projection to the intended columns) and a different need (A raw events dataset must remove exact duplicate records without altering other fields). It cannot establish that Use a LEFT JOIN from customers to transaction aggregates met the stated goal.

Use `ROW_NUMBER()` with `PARTITION BY customer_id` and deterministic order belongs to the separate case “Per-customer latest event is needed while retaining the full winning row,” where Only the highest-ordered event row per customer is selected would be expected. That differs from the condition in this stem: “A raw events dataset must remove exact duplicate records without altering other fields.”

The alternative Use `GROUP BY product_id, sale_date` with `SUM(amount)` has a distinct purpose—Sales totals should be calculated by product and calendar date. It would not establish the required outcome (A raw events dataset must remove exact duplicate records without altering other fields); the necessary verification concerns Identical duplicate rows collapse to one while distinct rows stay.

The alternative Use a ranking window with `PARTITION BY region` has a distinct purpose—Records need to be ranked within each region without collapsing individual rows. It would not establish the required outcome (A raw events dataset must remove exact duplicate records without altering other fields); the necessary verification concerns Identical duplicate rows collapse to one while distinct rows stay.

Only the highest-ordered event row per customer is selected would demonstrate the separate action Use `ROW_NUMBER()` with `PARTITION BY customer_id` and deterministic order, which addresses “Per-customer latest event is needed while retaining the full winning row.” It does not verify the requirement “A raw events dataset must remove exact duplicate records without altering other fields” following Apply a full-record DISTINCT projection to the intended columns.

The output includes customers with null transaction values would demonstrate the separate action Use a LEFT JOIN from customers to transaction aggregates, which addresses “Customers with no transactions must remain in a customer enrichment result.” It does not verify the requirement “A raw events dataset must remove exact duplicate records without altering other fields” following Apply a full-record DISTINCT projection to the intended columns.

This signal belongs to the adjacent case “Records need to be ranked within each region without collapsing individual rows,” whose remedy is Use a ranking window with `PARTITION BY region`. The observed result for the present requirement is Identical duplicate rows collapse to one while distinct rows stay.