Prepare the data

Which TWO actions can improve data refresh performance in Power BI?

A company has a Power BI dataset that imports data from a SQL Server database. The dataset includes a table with 10 million rows. The data model uses a single table and does not include any calculated columns or measures. The report users report that the dataset refresh takes too long. Which action should you take to improve refresh performance?

You are preparing data for a Power BI report. You have a table that contains a 'ProductID' column with some null values. You need to ensure that the 'ProductID' column does not contain any null values in the data model. Which Power Query transformation should you apply?

You are creating a Power BI report from a SQL Server database that contains a table Orders with columns: OrderDate, CustomerID, ProductID, Quantity, UnitPrice. You need to build a star schema. Which THREE tables should you create? (Choose three.)

You are working on a Power BI project for a marketing department. You have a CSV file with customer survey responses. The file contains columns: CustomerID, SurveyDate, Response (text with ratings from 1 to 5), Comments (free text). The file is 10 MB. You need to load the data into Power BI and create a measure that calculates the average rating. However, when you load the file, you notice that the Response column is imported as text instead of whole number. Also, there are some rows with missing values in the Response column. You need to ensure the data is correctly typed and handle missing values appropriately. What is the best approach?

You have a Power BI dataset that includes a date table created using CALENDAR(). You need to ensure that the date table always covers the full range of dates present in the fact table, even after new data is loaded. What should you do?

Your Power BI dataset uses a SQL view that joins multiple tables. You notice that some columns have null values where you expect data. You suspect the view definition has a bug. How can you verify the view's output in Power Query?

You are preparing data from a SQL Server database. The table 'Sales' contains a column 'OrderDate' that includes both date and time (e.g., '2023-10-15 14:30:00'). You need to create a separate column for the time portion only. Which TWO Power Query transformations can you use?

You are using the above KQL query as a source in Power Query for a Power BI semantic model. The query runs successfully but takes a long time to execute. You need to improve performance. What should you do?

You are designing a Power BI semantic model that uses a large fact table from Azure SQL Database. The table includes a date column. You need to ensure that the model supports time intelligence functions like TOTALYTD and SAMEPERIODLASTYEAR. What is the recommended approach?

You are configuring a Power BI dataset with incremental refresh. The above JSON shows part of the M script parameters. The dataset uses a single SQL Server data source. You need to ensure that incremental refresh works correctly. What must you do?

You are importing data from a CSV file that contains a column 'OrderDate' with dates in the format 'MM/dd/yyyy'. Some rows have invalid dates like '02/30/2023'. What is the best way to handle these errors in Power Query?

You are preparing data from multiple sources for a Power BI report. You need to create a star schema with a single fact table and several dimension tables. Which of the following is a best practice when designing the data model?

You are connecting to a data source that contains Personally Identifiable Information (PII). You need to ensure that only authorized users can view the data in Power BI reports. Which TWO actions should you take?

You are connecting to a SharePoint folder containing 100 Excel files. Each file has a similar structure but different column names. What is the best practice to combine these files into a single table while preserving the data?

You are reviewing a Power BI dataset configuration in the service. The JSON shows a data source for an Azure SQL Database. Which statement about the configuration is correct?

You are importing data from a CSV file that contains a column 'Date' with values like '2026-01-15'. After loading, Power Query detects the column as type 'text'. What is the recommended step to ensure the column is treated as a date?

You are loading data from an Excel workbook that has multiple sheets. Each sheet contains sales data for a different region. You need to combine all sheets into one table. What is the most efficient approach?

Which THREE of the following are best practices for optimizing data load performance in Power BI?

You are preparing data from a CSV file that has inconsistent date formats. Some rows use 'MM/dd/yyyy' and others use 'dd/MM/yyyy'. You need to parse all dates correctly. What is the best approach in Power Query?

You are building a Power BI data model that combines Sales data from SQL Server and Marketing data from a CSV file. The Sales table has a unique 'OrderID' column, and the Marketing table has a 'CampaignID' column. You need to create a relationship between Sales and Marketing to analyze campaign effectiveness. What should you do?

Refer to the exhibit. The Power Query M code connects to a SQL Server database and performs data transformation. However, the query is failing with a privacy level error. What is the most likely cause?

You are importing data from an Excel workbook that contains multiple sheets. You only need data from the 'Sales' sheet. In Power Query Editor, what should you do to load only that sheet?

Which TWO actions can you take in Power Query Editor to improve data quality and reduce load time? (Choose two.)

Which TWO are valid methods to handle null values in Power Query? (Choose two.)

Which TWO are best practices for optimizing Power Query performance? (Choose two.)

You are importing data from a CSV file into Power BI. The file contains a column 'SalesAmount' with values like '$1,234.56'. When you load the data, the column is detected as text. What is the most efficient way to convert this column to a numeric type in Power Query?

You are reviewing a Power BI data source configuration in the data source settings. The exhibit shows the JSON representation of a data source. Which issue might arise from this configuration?

You are preparing data from an Excel workbook that contains multiple sheets. Each sheet has a similar structure but different data. You need to combine all sheets into a single table in Power Query. What is the most efficient approach?

You are reviewing a Power Query M expression that transforms column types. The 'SalesAmount' column contains values like '1,234.56' (with a comma as thousands separator). After applying this transformation, what is the likely result?

You are importing data from a folder containing multiple CSV files with identical structure. You want to automatically combine all files into one table in Power Query. Which connector should you use?

You are preparing a data model that uses a date table. You need to ensure that the date table includes all dates from January 1, 2020 to December 31, 2025. What is the most efficient way to create this date table in Power Query?

You have a Power Query query that loads data from an OData source. You need to reduce the amount of data loaded into the data model. What is the best practice?

You are preparing data for a Power BI report that requires a date table with continuous dates from 2020 to 2025. Which TWO methods can you use to create this date table in Power Query?

You are importing data from a SQL Server database. The source table has a column 'ModifiedDate' of type datetime2. In Power Query, you want to ensure that only rows modified within the last 7 days are loaded. Which THREE steps should you take?

You are reviewing a Power Query that imports data from SQL Server. The exhibit shows the M code. The SQL query filters records after a date, then Power Query filters rows with OrderQty > 10, and then groups by ProductID. What is a potential performance issue with this approach?

You connect to a large Azure SQL Database table with over 100 million rows. You need to create a report that shows sales by month for the current year only. Which data reduction technique should you use in Power Query to minimize data load?

You have a table with a column 'FullName' that contains names in the format 'Last, First'. You need to split this column into 'LastName' and 'FirstName' columns. Which Power Query transformation should you use?

You are connecting to a SQL Server database using Import mode. The source table contains a column 'SalesAmount' with a few null values. You need to replace nulls with 0 before loading. What is the most efficient step to achieve this in Power Query Editor?

You have a Power BI dataset that uses data from Microsoft Excel files stored in SharePoint Online. Users report that the data is not refreshing as scheduled. You verify that the gateway is installed and running. What is the most likely cause of the refresh failure?

Which TWO of the following are valid reasons to use a calculated column instead of a measure in Power BI? (Select exactly two.)

Your Power BI dataset uses DirectQuery to a SQL Server data warehouse. Users report that reports are slow. You need to improve performance without changing the data source. What should you do?

You have a dataset with a column 'FullName' containing values like 'John Doe'. You need to split this column into 'FirstName' and 'LastName' using the space delimiter. Which Power Query transformation should you use?

You are designing a data model in Power BI. You have a Sales table and a Date table. The Date table should contain all dates from 2020 to 2025. What is the best practice for creating the Date table?

You are connecting to an Azure SQL database using DirectQuery. The database has a large table with millions of rows. Users need to see aggregated data quickly. What should you implement to improve query performance?

You are building a Power BI semantic model that combines data from an on-premises SQL Server database and a SharePoint Online list. The SQL Server table contains 10 million rows and updates hourly. The SharePoint list contains 500 rows and updates daily. You need to minimize the data load time and ensure the model refreshes within the scheduled 30-minute window. What should you do?

You receive a Power Query error: 'Expression.Error: The key didn't match any rows in the table.' This occurs when merging two queries. What is the most likely cause?

You need to connect Power BI to an Excel file stored on a local network drive. The file is updated manually each morning. You want the Power BI report to always show the latest data when opened. Which data connectivity mode should you choose?

You are preparing data for a Power BI report. The source data contains a column 'FullName' with values like 'John Doe'. You need to split this column into 'FirstName' and 'LastName' using Power Query. The transformation should be repeatable and not dependent on the number of spaces. What is the best approach?

You are using Power Query to combine data from multiple CSV files in a folder. Each file has the same structure. You want to append all rows into a single table. Which Power Query function should you use?

You need to create a Power BI data model that includes a date dimension. The source data contains a table with a Date column covering 2015-2025. You want to ensure that all dates in the model have a contiguous range for time intelligence. What should you do?

You have a Power BI semantic model that uses Import mode with a SQL Server data source. The refresh takes over two hours. You need to reduce the refresh time while keeping data up-to-date. What is the best strategy?

You are importing data from a SQL Server view into Power BI. The view contains calculated columns that are expensive to compute. You want to minimize the load on the source database during refresh. What should you do?

Refer to the exhibit. You are reviewing a DAX measure in Power BI. The measure is intended to calculate total sales for the year 2024. However, when used in a visual with a slicer on 'Sales[Date]', the measure does not respect the slicer selection. What is the most likely reason?

Refer to the exhibit. You are configuring a scheduled refresh for a Power BI dataset. The exhibit shows the refresh schedule settings. The dataset is in a workspace in a Premium capacity. The scheduled refresh runs at 5:00 AM UTC daily. However, the refresh is failing consistently. What is the most likely cause?

You are building a Power BI data model from an Azure SQL Database. The source table contains a column 'OrderDate' of type datetime. You want to create a date table in Power Query that includes all dates from the minimum to maximum OrderDate. Which M function should you use to generate the list of dates?

You are importing data from a CSV file into Power BI. The file contains a column 'SalesAmount' with values like '1,234.56' and '(987.65)' for negative amounts. You need to transform this column into a decimal number. Which sequence of Power Query steps achieves this?

You have a Power BI semantic model that uses DirectQuery to an Azure Synapse Analytics dedicated SQL pool. The model is used by a real-time dashboard. Users report that the dashboard is slow. You need to improve query performance without changing the source system. Which action should you take?

You are using Power Query to combine data from multiple Excel files stored in a SharePoint Online document library. Each file has the same structure. You need to ensure that the query automatically includes new files added to the library without manual updates. Which approach should you use?

You are preparing data for a Power BI report. The source data contains a 'CustomerName' column with values like 'John, Doe'. You need to split this column into two columns: 'FirstName' and 'LastName'. The comma is used as a delimiter, but some names have a space after the comma. Which split method should you use?

Which TWO of the following are valid data source types in Power BI that support DirectQuery? (Select TWO.)

Which THREE of the following are best practices for data preparation in Power BI to improve performance and maintainability? (Select THREE.)

Refer to the exhibit. You are reviewing a Power BI data source credential configuration. The Azure Blob Storage data source uses 'Anonymous' credentials. However, the refresh fails with an error indicating that the blob container is private and requires authentication. Which change should you make?

Your organization uses Power BI to analyze sales data stored in Azure SQL Database. The data model includes a fact table with millions of rows. To improve performance, you need to reduce the amount of data loaded into the model. Which action should you take?

You are preparing data for a Power BI report. The source data contains a column with mixed data types: some values are numbers, others are text. When loading into Power Query, the entire column is typed as text. What is the likely cause?

You are designing a Power BI solution that ingests data from multiple sources: Azure Blob Storage, Salesforce, and an on-premises Oracle database. The data must be combined into a single semantic model. The Oracle database contains sensitive customer information that must be masked before being loaded. Which approach should you use to prepare the data?

You are importing a CSV file into Power BI. The file contains a date column with values in the format 'MM/dd/yyyy'. However, Power Query interprets the dates as 'dd/MM/yyyy'. What should you do to correctly parse the dates?

You need to combine two tables from different sources: 'Orders' from SQL Server and 'Returns' from an Excel file. Both tables have a column named 'OrderID'. You want to include all orders and only matching returns. Which join type should you use in Power Query?

You are building a Power BI semantic model that uses a large fact table from a data warehouse. The fact table has a date column and you want to create a date dimension. The organization requires that the date dimension includes all dates from 2010 to 2030, including weekends and holidays. What is the best practice for creating the date dimension?

You are connecting to an Azure SQL Database from Power BI Desktop. The database contains a view that returns thousands of rows. You only need the last 100 rows for analysis. What is the most efficient way to reduce the data loaded?

Which TWO data source types can be used with Power BI dataflows?

You are troubleshooting a Power Query transformation that groups sales data by ProductID. The query runs slowly and you suspect the filter is being applied after loading all rows. What change would improve performance by pushing the filter to the source?

You are preparing a Power BI report that uses data from Azure SQL Database. The data includes a date column that needs to be used in time intelligence calculations. You want to ensure that the date column is recognized as a date table in the data model. What should you do?

You are importing data from a CSV file that contains a column with mixed data types (numbers and text). Power BI automatically assigns the data type as Text. You need to perform numerical aggregations on this column. What should you do?

You are importing data from an Excel workbook that contains multiple sheets. Each sheet has similar structure but different data for different regions. You need to combine all sheets into a single table for analysis. What should you do?

You are preparing data for a Power BI report. The source data contains a column with values like '1,234.56' formatted as text. You need to convert this to a numeric value for calculations. What is the best approach?

Which TWO of the following are valid methods to combine data from multiple sources in Power BI?

Which TWO of the following are valid ways to create a date table in Power BI?

You are a data analyst at a retail company. You are building a Power BI report to analyze sales performance across multiple stores. The source data comes from an Azure SQL Database that contains a table 'Sales' with columns: StoreID, ProductID, SaleDate, Quantity, and Amount. The database also has a 'Stores' table with StoreID and StoreName, and a 'Products' table with ProductID, ProductName, and Category. You need to create a data model that supports filtering by store, product category, and date, and also allows calculation of year-over-year sales growth. You want to minimize the model size and ensure optimal performance. The data volume is large (millions of rows). You must design the data model. What should you do?

You are a Power BI developer for a financial services company. You are preparing data from multiple sources: a CSV file containing daily stock prices (ticker, date, close_price), a SQL Server database with company information (ticker, company_name, sector), and an Excel file with quarterly earnings data (ticker, quarter, earnings_per_share). The CSV file has 5 years of daily data (approx 1.3 million rows). The SQL Server table has 5000 rows. The Excel file has 20,000 rows. You need to create a data model that allows users to filter by sector, company, and date range, and to calculate moving averages of stock prices and compare earnings over time. Performance is critical. You must decide the best approach to combine and model this data. What should you do?

You are creating a Power BI dataset from a SQL Server data warehouse. The warehouse contains a fact table with 500 million rows and dimension tables. You need to minimize the data refresh time while ensuring that the dataset meets the reporting requirements. Which approach should you recommend?

You are transforming data in Power Query. A column named 'SalesAmount' contains values as text with a dollar sign and thousands separator, e.g., "$1,234.56". You need to convert this column to a decimal number for analysis. What is the most efficient sequence of transformations?

You are merging two queries in Power Query. Query 'Orders' contains columns: OrderID, CustomerID, OrderDate. Query 'Customers' contains columns: CustomerID, CustomerName, Segment. You need to add the CustomerName to the Orders query. The relationship between Orders and Customers is many-to-one. Which join kind should you use?

You have a Power BI dataset that uses Import mode and refreshes daily. The source data includes a column 'LastModifiedDate'. You want to reduce the amount of data loaded during each refresh by only loading rows that have changed since the last refresh. Which feature should you configure?

You are connecting to a SharePoint folder that contains Excel workbooks. Each workbook has multiple sheets. You need to combine data from a specific sheet named 'Sales' across all workbooks. Which Power Query approach should you use?

You are reviewing a Power Query query that loads data from a SQL Server database. The query includes multiple steps that perform data transformation. You want to ensure that the query is optimized by pushing as many transformations as possible to the SQL Server. What should you look for?

You are preparing data from an Azure SQL Database. You need to ensure that sensitive columns (e.g., Social Security Numbers) are obfuscated in Power BI reports. Which TWO of the following approaches can you use? (Choose two.)

You are connecting to an on-premises Oracle database from Power BI Service. The gateway is installed and configured. However, the scheduled refresh fails with an error indicating that the data source credentials are invalid. Which TWO steps should you take to resolve the issue? (Choose two.)

You are a data analyst for a global retail company. The company uses Power BI Premium capacity. You are building a dataset that combines sales data from three sources: 1. An Azure SQL Database that stores transactional sales data (10 million rows per day, retained for 5 years). 2. A SharePoint Online folder containing monthly Excel reports from regional offices (each report has a different structure). 3. A Dataverse table that contains customer feedback scores. Requirements: - The dataset must support near real-time reporting for the current month's sales (maximum 15-minute latency). - Historical sales data (older than current month) can be refreshed daily. - Customer feedback scores should be updated every hour. - The Excel reports from SharePoint must be combined into a single table with consistent columns. - The final dataset should be optimized for fast query performance. You need to design the data preparation strategy. What should you do?

You are a Power BI developer for a healthcare organization. You are building a dataset that includes patient data from an on-premises SQL Server database. The database contains a table 'PatientVisits' with columns: PatientID, VisitDate, DiagnosisCode, and Cost. The database also has a table 'DiagnosisLookup' with DiagnosisCode and Description. You need to create a star schema in Power BI. The requirements are: - The dataset must include a date dimension table that covers all dates from 2010 to 2030. - The 'PatientVisits' table should be the fact table. - Diagnosis descriptions should be in a dimension table. - You must use Power Query to create the date dimension table using M code. - The data refresh must be scheduled daily via the on-premises data gateway. You have already loaded the 'PatientVisits' and 'DiagnosisLookup' tables. What should you do next to complete the star schema?

You are merging two queries in Power Query: 'Orders' and 'Customers'. The 'Orders' table has a 'CustomerID' column, and 'Customers' has 'CustomerID' and 'Name'. You need to bring the 'Name' into 'Orders' but only for matching CustomerIDs; unmatched rows should be removed. Which join kind should you use?

You have a Power BI report that uses a date table connected to a fact table. You need to ensure that all dates in the fact table are covered by the date table. Which relationship property should you configure?

You are importing data from a folder containing multiple Excel files with the same structure. You use Power Query's 'Combine Files' feature. Which TWO statements about this process are correct?

You are a data analyst for a multinational corporation. You are building a Power BI report that uses a large fact table (100 million rows) and several dimension tables. The data source is a SQL Server data warehouse. Users need to see near real-time data with a maximum latency of 15 minutes. The current import mode takes too long to refresh. You decide to use DirectQuery mode. However, queries are slow. You need to improve query performance. You consider creating aggregations in the data source. Which approach should you take in Power BI to leverage these aggregations?

You are a data analyst at a retail company. You have a Power BI semantic model that imports sales data from an Azure SQL Database. The database uses a timestamp column to track transaction time. You need to reduce the data refresh time and ensure that only the last 30 days of data are refreshed during each scheduled refresh. You have already created the necessary parameters rangeStart and rangeEnd in Power Query. What should you do next to implement incremental refresh?

You are building a Power BI report for a manufacturing company. You have a large fact table with 50 million rows in Azure SQL Database. You need to minimize the data refresh time and ensure that only new or changed rows are loaded. The source table has a LastModifiedDate column. What should you do?