PL-300 · Question #327
Case Study 1 - Litware, Inc. Overview Litware, Inc. is an online retailer that uses Microsoft Power Bl dashboards and reports. The company plans to leverage data from Microsoft SQL Server databases, M
The correct answer is B. a measure that uses the following formula: SUMX(FILTER('Sales', 'Sales'[sales_amount] >. The scenario involves data quality concerns around the sales_amount column, which may contain negative values or blanks representing erroneous records. Option B - SUMX(FILTER('Sales', 'Sales'[sales_amount] > 0), Sales[sales_amount]) - is correct because it uses FILTER to exclude
Question
Case Study 1 - Litware, Inc. Overview Litware, Inc. is an online retailer that uses Microsoft Power Bl dashboards and reports. The company plans to leverage data from Microsoft SQL Server databases, Microsoft Excel files, text files, and several other data sources. Litware uses Azure Active Directory (Azure AD) to authenticate users. Existing Environment Sales Data Litware has online sales data that has the SQL schema shown in the following table. In the Date table, the dateid column has a format of yyyymmdd and the month column has a format of yyyymm. The week column in the Date table and the weekid column in the Weekly_Returns table have a format of yyyyww. The regionid column can be managed by only one sales manager. Data Concerns You are concerned with the quality and completeness of the sales data. You plan to verify the sales data for negative sales amounts. Reporting Requirements Litware identifies the following technical requirements:
- Executives require a visual that shows sales by region.
- Regional managers require a visual to analyze weekly sales and
returns.
- Sales managers must be able to see the sales data of their respective
region only.
- The sales managers require a visual to analyze sales performance
versus sales targets.
- The sale department requires reports that contain the number of sales
transactions.
- Users must be able to see the month in reports as shown in the
following example: Feb 2020.
- The customer service department requires a visual that can be
filtered by both sales month and ship month independently.
- The maximum allowed latency to include transactions in reports is
five minutes. What should you do to address the existing environment data concerns?
Options
- Aa calculated column that uses the following formula: ABS(Sales[sales_amount])
- Ba measure that uses the following formula: SUMX(FILTER('Sales', 'Sales'[sales_amount] >
- Ca measure that uses the following formula: SUM(Sales[sales_amount])
- Da calculated column that uses the following formula: IF(ISBLANK(Sales[sales_amount]),0,
How the community answered
(45 responses)- A2% (1)
- B73% (33)
- C7% (3)
- D18% (8)
Explanation
The scenario involves data quality concerns around the sales_amount column, which may contain negative values or blanks representing erroneous records. Option B - SUMX(FILTER('Sales', 'Sales'[sales_amount] > 0), Sales[sales_amount]) - is correct because it uses FILTER to exclude non-positive values (negatives and zeros) before summing, ensuring only valid sales amounts are aggregated. Option A (ABS on a calculated column) would convert negative values to positive, distorting actual sales figures rather than excluding bad data. Option C (SUM) would include negative values, producing incorrect totals. Option D (IF(ISBLANK(...),0,...) as a calculated column) handles only blank values, not negatives, and as a calculated column it adds redundant storage to the model rather than computing dynamically as a measure.
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