An e-commerce company uses Looker to analyze transaction data. A data analyst needs to update the orders.view file to add a new field that calculates the total monetary revenue by summing the values of an existing sale_price dimension. Which LookML code block correctly defines this aggregate field?

measure: total_sales { type: sum sql: ${sale_price} ;; }: ### What the `measure` parameter is In LookML, a **`measure`** defines an aggregate computation or measurable metric across multiple rows of data, such as sums, averages, counts, or distinct counts. While dimensions represent row-level attributes, measures perform SQL aggregations that summarize data when queried. ### How it meets the business requirement - **Aggregate computation (✓):** Declaring the field with `measure: total_sales` tells Looker that this field represents an aggregated calculation rather than a non-aggregate row-level attribute. - **Sum calculation (✓):** Specifying `type: sum` instructs Looker to generate a SQL `SUM()` aggregation in the underlying database query. - **LookML field reference (✓):** The `sql: ${sale_price} ;;` syntax uses Looker's **substitution operator** (`${field_name}`) to reference the pre-existing `sale_price` dimension, adhering to sustainable modeling practices and code reusability. ### Key advantages and capabilities - **Automatic SQL generation:** Looker dynamically creates the appropriate SQL `GROUP BY` and `SUM()` syntax based on any dimensions selected alongside this measure in an Explore. - **Maintainability:** Reusing the existing `${sale_price}` dimension ensures that if the underlying database column mapping ever changes, only the base dimension definition requires updating. - **Reusability:** This measure can be reused in subsequent LookML calculations (such as calculating profit margins or average order values) using the same substitution syntax. - **Business user accessibility:** Once defined in the view file, `Total Sales` appears under the measures section in the field picker, enabling non-technical users to build reports without writing SQL. ### Why this is optimal Declaring an aggregate metric as a `measure` of `type: sum` referencing `${sale_price}` is the standard, valid LookML pattern for computing numeric totals from dimensions.