PL-300 Question #141: Real Exam Question with Answer & Explanation

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. You need to review the data for which there are concerns before creating the data model. What should you do in Power Query Editor?

Options

• ATransform the sales_amount column to replace negative values with 0.
• BSelect Column distribution.
• CSelect the sales_amount column and apply a number filter.
• DSelect Column profile, and then select the sales_amount column.

Explanation

The goal is to investigate data quality and completeness issues in the sales_amount column. Option D - selecting 'Column profile' and then selecting the sales_amount column - provides the most comprehensive column-level statistics: value distribution, min/max, count of errors, count of empty values, and distinct value counts. This is the correct tool for diagnosing quality issues. Option A (replacing negatives with 0) is a transformation/fix, not investigation. Option B (Column distribution) gives a high-level histogram but lacks the detailed statistics needed for quality analysis. Option C (applying a number filter) filters data rather than profiling it. Column profile is the feature specifically designed for in-depth data quality assessment.

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