Summary
Highlights
Another question on improving Power BI report performance for a large PBIX file and slow visuals. The recommended solution again is to remove unused columns from tables in the data model.
This section explains how to prepare a 'logged' column from a CSV file (containing date and time information) to analyze complaints by date using a built-in date hierarchy. The solution involves creating a column by example and setting its data type to date.
This section demonstrates how to create a calculated column in Power BI that returns the count of levels from each employee to the CEO in an organizational hierarchy, using a specific DAX expression.
This question focuses on creating a calculated column named 'active store name' in Power BI. The column should return the store name if its status is 'a' or prefix it with 'inactive' otherwise, using a DAX expression.
This section re-visits the CSV file with user complaints and a 'logged' column. The correct action to enable analysis by logged date with a built-in date hierarchy is to split the logged column by a delimiter.
This part details how to create a DAX measure to count the number of product categories that have products sold during a selected period in a Power BI data model.
This section provides a third scenario for improving Power BI report performance with a large file and slow visuals. The consistent recommendation is to remove unused columns from tables in the data model.
This part evaluates the impact of removing columns from a sales table on model size while supporting specific sales analysis requirements. Removing 'Last Updated' and 'Ship Date' columns reduces model size while supporting analysis, but removing 'Product ID' does not.
This question again focuses on preparing the 'logged' column for date hierarchy analysis. The correct solution is to create a column by example and set its data type to date.
This section explains how to create a table filter for dynamic RLS, ensuring users see only their own employee data and the DAX expression works in both Power BI Desktop and Service.
This part involves dragging and dropping DAX expressions to configure RLS for managers (seeing data for their country in sales and HR) and the CFO (seeing all sales data but no HR data).
This question addresses common causes for a Power Query error 'Expression.Error: The column 'Category' of the table wasn't found' after reloading a query with renamed and custom columns. Possible causes are the column being removed or renamed in the source file.
This section focuses on selecting appropriate storage models (Import, DirectQuery, Dual) for different tables in a Power BI model based on varied refresh requirements (daily, every 3 years, near real-time, weekly) to minimize visual load times and ensure data loading.
This segment identifies the correct connector to use when creating a Power BI report that connects to a project management app fully hosted in Microsoft Teams and developed with Power Apps. The correct connector is Dataverse.
This question asks about minimizing development effort when creating a new Power BI report from an existing report that imports data from an Excel file in SharePoint and contains measures. The recommended data source type is a Power BI dataset.
This section explains how to combine 'customer' and 'address' tables in Power Query to create a single table with one row per customer, including city, state/region, and country. The solution involves merging the customer and address tables.
This question addresses combining customer tables from two Azure SQL databases into a single table, minimizing data model size, and supporting scheduled refresh. The correct approach needs to be selected from the options.
This part involves dragging and dropping appropriate merge types to combine 'product category', 'product subcategory', and 'product' queries in Power Query Editor, ensuring the best performance and handling relationships where not all subcategories have parent categories.
This question focuses on combining products from two tables in different sheets of an Excel workbook into a single column without duplicate values using Power Query Editor. Three sequential actions must be performed.
This section deals with configuring a data connection for an Azure SQL database with frequently updated sales transactions, where reports need to show data within 5 minutes of an update. The solution is to set the data connectivity mode to Direct Query.
This part describes how to analyze the frequency distribution of string length in a free text field ('call one') without affecting the model size. The solution is to change the distribution for the column profile to 'group by length' in Power Query Editor.
This question involves completing a DAX calculation to create a measure that calculates the total sales from the equivalent month of the previous year.
This section explains the outcome when a user is a member of two RLS roles with different filters (one on 'State Province' and another on 'Calendar Year'). The user will see data where either condition is met (OR logic).
This part focuses on optimizing a data model by hiding columns ('Employee ID' and 'Employee_Photo') that have no reporting requirements, thus meeting the solution goal.
This question explores importing a sample of data from a large SQL Server table during development. Adding a report-level filter based on order date does not meet the goal of importing a *sample* during the development process.
This section evaluates actions to reduce the data model size for sales reports while still supporting current analysis (total sales, order count, new/repeat customers). Removing 'unique price' and 'discount' columns reduces model size while preserving analysis; others do not.
This part discusses minimizing the size of a procurement report's dataset without affecting existing visuals. The solution is to remove the 'description' column from the 'line items' table.
This question involves completing a DAX calculation to create a calculated table named 'numbers' containing all integers from -100 to 100.
This section asks for the appropriate visualization type to compare revenue and cost over time. A line chart is the correct choice for this type of time-series comparison.
This part outlines the sequential actions to build a dashboard that will be frequently viewed in portrait mode on mobile phones, using existing reports.
This question describes creating a reference line in a clustered bar chart to show employees above the median salary. Creating a percentile line at 50% using the salary measure meets this goal.
This section asks what event automatically refreshes dashboard tiles from an imported dataset. The correct answer is when the dashboard tile is refreshed.
This part queries the best visualization type to identify outliers in a table containing sales data with approximately 1,000 rows. A scatter plot is the most appropriate visualization for this.
This question focuses on increasing the likelihood of anomaly detection identifying anomalies in a Power BI report's visual. The solution is to increase the sensitivity setting of the anomaly detection.
This section explains how to create a Power BI theme for corporate branding (font size, color, bar chart formatting) that can be used across multiple reports. The solution is to create a theme as a JSON file and import it.
This part details the three sequential actions needed to prepare a custom tooltip page for use in a Power BI report: setting 'Allow use as tooltip' to on, configuring filters for the page, and adding/configuring visuals on the page.
This question involves dragging and dropping appropriate user roles to grant specific capabilities in a Power BI workspace, adhering to the principle of least privilege.
This question discusses supporting sales analysis based on multiple date foreign keys (due date, order date, delivery date). Adding inactive relationships alone does not meet the goal; measures using USERELATIONSHIP DAX function are typically required.
This part identifies what needs to be created in a Power BI model to allow analysts to quickly build drill-downs from business unit to product in a visual. The solution is to create a hierarchy.
This question asks for the minimum number of Power BI datasets needed to support specific reports. The correct answer is two imported data sets.
This section focuses on ensuring data is updated to meet report requirements with minimal configuration effort. The solution is to configure a scheduled refresh without using an on-premises data gateway.
This part asks how to distribute Power BI reports to the board. The appropriate options need to be selected in the answer area.
This question involves selecting the appropriate options to configure access for a business unit analyst.
This section requires identifying what to create to meet the reporting requirements of the sales department.
This part addresses statements about refreshing and authenticating a published dataset. An on-premises data gateway and scheduled refresh are often needed, but basic authentication on the dataset might not be an option.
This question focuses on how to create a KPI visualization to meet the reporting requirements of sales managers, by selecting appropriate options in the answer area.
This section revisits supporting sales analysis over time based on multiple date foreign keys. Creating measures that use the USERELATIONSHIP DAX function on an active relationship between sales and date tables meets the goal.
This question asks what needs to be done to create a relationship in the dataset for Row-Level Security (RLS) by selecting the appropriate options.
This part focuses on designing the data model in Power BI Desktop to meet report requirements, specifically concerning date columns. The correct approach is to add a date table from Power Query, create an active relationship to the order date, and an inactive relationship to the ship date.
This question involves completing a DAX expression to create a measure that returns the percentage of late orders.
This section evaluates statements about designing the data model and relationships between 'customer details' and 'orders' tables. A relationship between 'customer ID' columns is essential, but the data type doesn't necessarily need to be text, and the regional filter should come from an appropriate table.
This question addresses how to meet executive reporting requirements after merging sales region, region manager, sales manager, and manager data into a single 'region' table. The solution is to create a hierarchy in the region table using manager name and sales manager name.
This part questions a proposed solution for analyzing IoT events by hour and day of the year to improve dataset performance. The solution involves concatenating two ID columns and deleting the originals, but this does not meet the goal.
This section covers how to create a DAX measure to calculate the percent of total sales for each product, respecting report-level filters. The correct DAX functions need to be dragged to complete the measure.
This part discusses reducing the size of a Power BI data model that analyzes product sales over time, while maintaining the ability to analyze sales by month and quarter. The correct actions involve creating and marking a date table and disabling the auto date/time option.
This segment addresses how to improve the performance of a slow-loading Power BI report with a large PBIX file size, numerous visuals, and a large imported dataset. The recommended solution is to remove unused columns from tables in the data model.