Power BI + Power Query Quiz

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QUESTION 1. (data preview)

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

A. Select Column distribution.

B. Select the sales_amount column and apply a number filter.

C. Select Column profile, and then select the sales_amount column.

D. Transform the sales_amount column to replace negative values with 0.

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To review the data for concerns before creating the data model, you should choose:

C. Select Column profile, and then select the sales_amount column.

This option provides detailed statistics and insights about the sales_amount column, including the distribution of values, the presence of any errors, and the count of distinct and unique values. This helps you identify any potential issues or anomalies in the data before proceeding with the data model.

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QUESTION 2. (multiple tables)

You import two Microsoft Excel tables named Customer and Address into Power Query.

Customer contains the following columns: Customer ID, Customer Name, Phone, Email Address, Address ID.

Address contains the following columns: Address ID, Address Line 1, Address Line 2, City, State/Region, Country, Postal Code.

The Customer ID and Address ID columns represent unique rows.

You need to create a query that has one row per customer. Each row must contain City, State/Region, and Country for each customer.

What should you do?

A. Merge the Customer and Address tables.

B. Transpose the Customer and Address tables.

C. Group the Customer and Address tables by the Address ID column.

D. Append the Customer and Address tables.

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To create a query that has one row per customer, with each row containing City, State/Region, and Country for each customer, you should choose:

A. Merge the Customer and Address tables.

Merging the tables allows you to combine the relevant columns from both tables based on the common column, Address ID. This will give you a single table where each row contains all the necessary information for each customer, including their city, state/region, and country.

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QUESTION 3. (multiple tables)

You are building a Power Bl report that uses data from an Azure SQL database named erp1. You Import the following tables:

You need to perform the following analyses: (1) Orders sold over time that include a measure of the total order value, (2) Orders by attributes of products sold The solution must minimize update times when interacting with visuals in the report. What should you do first?

A. From Power Query, merge the Orders query and the Order Line Items query.

B. Calculate the count of orders per product by using a DAX function.

C. Create a calculated column that adds a list of product categories to the Orders table by using a DAX function.

D. From Power Query, merge the Order Line Items query and the Products query.

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To perform the analyses you need while minimizing update times when interacting with visuals in the report, you should choose:

D. From Power Query, merge the Order Line Items query and the Products query.

Merging the Order Line Items query with the Products query will allow you to create a comprehensive dataset that includes product attributes along with the order details. This will enable you to analyze orders by product attributes efficiently. Additionally, it helps in calculating the total order value by having all necessary details in one place, which can then be aggregated over time.

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QUESTION 4. (query optimization)

You have a Power BI dataset that contains a table named Temperature Readings.

The table has 12 million rows. All the columns are needed for analysis.

You need to optimize the dataset to decrease the model size. The solution must not affect the precision of the data. What should you do?

A. Split the DateTime column into separate date and time columns.

B. Disable the Power Query load.

C. Round the Longitude column two decimal places.

D. Change the data type of the TempCelsius column to Integer.

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To optimize the dataset and decrease the model size without affecting the precision of the data, you should choose

A. Split the DateTime column into separate date and time columns.

This approach can help reduce the storage size because date and time columns separately often take up less space than a combined DateTime column. Additionally, it maintains the precision of the data, which is crucial for your analysis.

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QUESTION 5. (data transformation)

You create the following step by using Power Query Editor.

=Table.ReplaceValue(
SalesLT_Address,
"1318",
"1319",
Replacer.ReplaceText,{"AddressLine 1"})

A row has a value of 21318 Lasalle Street in the AddressLine1 column.

A row has a value of 21318 Lasalle Street in the AddressLine1 column. What will the value be when the step is applied?

A. 1318.

B. 1319.

C. 21318 Lasalle Street.

D. 21319 Lasalle Street.

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When the step is applied, the value in the AddressLine1 column will be 21319 Lasalle Street.

The Table.ReplaceValue function replaces occurrences of "1318" with "1319" in the AddressLine1 column. Since "21318 Lasalle Street" contains "1318" within it, the replacement will result in "21319 Lasalle Street".

So, the correct answer is

D. 21319 Lasalle Street.

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QUESTION 6. (data loading)

You use Power BI Desktop to load data from a Microsoft SQL Server database.

While waiting for the data to load, you receive the following error.

ERROR timeout expired

You need to resolve the error.

What are two ways to achieve the goal? Each correct answer presents a complete solution

NOTE: Each correct selection is worth one point.

A. Split long running queries into subsets Of columns and use power Query to the queries

B. Disable query folding on long running queries

C. Reduce number of rows and columns returned by each query.

D. Use Power Query to combine long running queries into one query.

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To resolve the "ERROR timeout expired" issue in Power BI Desktop when loading data from a Microsoft SQL Server database, you can:

[1] Split long-running queries into subsets of columns and use Power Query to the queries (Option A). This approach helps manage the load by breaking down complex queries into smaller, more manageable parts.

[2] Reduce the number of rows and columns returned by each query (Option C). By limiting the data retrieved, you can decrease the load time and avoid timeout errors

These methods help optimize the data retrieval process and minimize the chances of encountering timeout errors.

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QUESTION 7. (optimization)

You have a data model that contains many complex DAX expressions. The expressions contain frequent references to the RELATED and RELATEDTABLE functions. You need to recommend a solution to minimize the use of the RELATED and RELATEDTABLE functions. What should you recommend?

A. Merge tables by using Power Query.

B. Hide unused columns in the model.

C. Split the model into multiple models.

D. Transpose.

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To minimize the use of the RELATED and RELATEDTABLE functions in your data model, you should choose

A. Merge tables by using Power Query.

Merging tables in Power Query can help simplify your data model by combining related data into a single table, reducing the need for complex DAX expressions that reference RELATED and RELATEDTABLE functions. This approach can improve performance and make your model easier to manage.

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QUESTION 8. (data preview)

You import a large dataset to Power Query Editor. You need to identify whether a column contains only unique values.

Which two Data Preview options can you use?

Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point

A. Show whitespace

B. Column distribution

C. Column profile

D. Column quality

E. Monospaced

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To identify whether a column contains only unique values in Power Query Editor, you can use the following Data Preview options:

B. Column distribution: This option provides a visual representation of the distribution of values in the column, including the count of distinct and unique values.

C. Column profile: This option gives a detailed profile of the column, including statistics such as the count of distinct and unique values, which helps in identifying if all values are unique.

These options will help you determine if a column contains only unique values effectively.

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QUESTION 9. (data preview)

You open a query in Power Query Editor. You need to identify the percentage of empty values in each column as quickly as possible. Which Data Preview option should you select?

A. Show whitespace

B. Column profile

C. Column distribution

D. Column quality

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A: To quickly identify the percentage of empty values in each column in Power Query Editor, you should select:

D. Column quality

This option provides a quick overview of the data quality for each column, including the percentage of valid, error, and empty values. It's the fastest way to see the percentage of empty values at a glance.

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QUESTION 10. (data analysis)

You are creating a report in Power BI Desktop. You load a data extract that includes a free text field named coll. You need to analyze the frequency distribution of the string lengths in col1. The solution must not affect the size of the model. What should you do?

A. In the report, add a DAX calculated column that calculates the length of col1

B. In the report, add a DAX function that calculates the average length of col1

C. From Power Query Editor, add a column that calculates the length of col1

D. From Power Query Editor, change the distribution for the Column profile to group by length for col1

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To analyze the frequency distribution of the string lengths in col1 without affecting the size of the model, you should choose:

A. In the report, add a DAX calculated column that calculates the length of col1.

Adding a DAX calculated column in the report allows you to perform the analysis directly within Power BI without significantly impacting the model size. This approach ensures that the model remains optimized while providing the necessary insights.

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QUESTION 11. (data modeling)

You have sales data in a star schema that contains four tables named Sales, Customer, Date, and Product. The Sales table contains purchase and ship dates. Most often, you will use the purchase date to analyze the data, but you will analyze the data by both dates independently and together. You need to design an imported dataset to support the analysis. The solution must minimize the model size and the number of queries against the data source. Which data modeling design should you use?

A. Use the Auto Date/Time functionality in Microsoft Power Bl and do NOT import the Date table.

B. Duplicate the Date query in Power Query and create active relationships between Sales and both Date tables in the modeling view.

C. On the Date table, use a reference query in Power Query and create active relationships between Sales and both Date tables in the modeling view.

D. Import the Date table twice in Power Query and create active relationships between Sales and both Date tables in the modeling view.

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To design an imported dataset that supports analyzing sales data by both purchase and ship dates while minimizing the model size and the number of queries against the data source, you should choose:

C. On the Date table, use a reference query in Power Query and create active relationships between Sales and both Date tables in the modeling view.

Using a reference query in Power Query allows you to create a second instance of the Date table without duplicating the data, which helps minimize the model size. You can then create active relationships between the Sales table and both Date tables, enabling you to analyze the data by both purchase and ship dates independently and together.

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QUESTION 12. (data preview)

You are reviewing a query that produces 10,000 rows in the Power Query Editor. You need to identify whether a column contains only unique values. Which two Data Preview options can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. Column profile

B. Column distribution

C. Show whitespace

D. Column quality

E. Monospace

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To identify whether a column contains only unique values in Power Query Editor, you can use the following two Data Preview options:

A. Column profile: This option provides detailed statistics about the column, including the count of distinct and unique values, which helps you determine if all values are unique.

B. Column distribution: This option shows a visual representation of the distribution of values in the column, including the count of distinct and unique values, making it easy to spot if there are any duplicates.

These options will give you the necessary insights to determine the uniqueness of the values in the column.

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QUESTION 13. (data modeling)

You need to design the data model to meet the report requirements. What should you do in Power Bl Desktop?

A. From Power Query, add columns to the Orders table to calculate the calendar quarter and the calendar month of the OrderDate column.

B. From Power Bi Desktop, use the Auto date/time option when creating the reports.

C. From Power Query, add a date table. Create an active relationship to the OrderDate column in the Orders table and an inactive relationship to the ShippedDate column in the Orders table.

D. From Power Query, use a DAX expression to add columns to the Orders table to calculate the calendar quarter of the OrderDate column, the calendar month of the OrderDate column, the calendar quarter of the ShippedDate column, and the calendar month of the ShippedDate column

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To design the data model to meet the report requirements in Power BI Desktop, you should choose:

C. From Power Query, add a date table. Create an active relationship to the OrderDate column in the Orders table and an inactive relationship to the ShippedDate column in the Orders table.

This approach allows you to use a single date table to handle both the OrderDate and ShippedDate columns. By creating an active relationship with the OrderDate and an inactive relationship with the ShippedDate, you can easily switch between the two dates in your reports using DAX functions like USERELATIONSHIP to activate the inactive relationship when needed. This method is efficient and keeps your model size optimized.

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QUESTION 14. (data preview)

You have a prospective customer list that contains 1,500 rows of data. The list contains the following fields: First name, Last name, Email address, State/Region, Phone number .

You import the list into Power Query Editor. You need to ensure that the list contains records for each State/Region to which you want to target a marketing campaign. Which two actions should you perform? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.

A. Open the Advanced Editor.

B. Select Column quality.

C. Enable Column profiling based on entire dataset.

D. Select Column distribution.

E. Select Column profile.

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To ensure that your list contains records for each State/Region you want to target in your marketing campaign, you should perform the following two actions:

B. Select Column quality: This option provides a quick overview of the data quality for each column, including the percentage of valid, error, and empty values. It helps you identify if there are any missing or incomplete records for the State/Region field.

C. Enable Column profiling based on entire dataset: By enabling this option, you ensure that the column profiling is done on the entire dataset rather than just a sample. This gives you a complete and accurate picture of the data distribution and quality across all 1,500 rows.

These actions will help you verify that you have records for each State/Region and ensure the data quality is sufficient for your marketing campaign.

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QUESTION 15. (data transformation)

Given the first three rows of a table:

Month, 2020, 2021
January, 4400, 4908
February, 2988, 3722

You need to shape the query to display the following three columns: 1. Month 2. Sales 3. Year.

What should you select in Power Query Editor?

A. Pivot column

B. Merge columns

C. Unpivot columns

D. Transpose

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To shape the query to display the columns "Month," "Sales," and "Year," you should select:

C. Unpivot columns

This option will transform the columns "2020" and "2021" into rows, creating a new column for "Year" and another for "Sales," while keeping the "Month" column intact. This will give you the desired structure:

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QUESTION 16. (data transformation)

You are building a data model for a Power Bl report. You have data formatted as shown in the following table.

You need to create a clustered bar chart; Machine as Axis and DownloadMB as Values. What should you do?

A. From Power Query Editor, split the Machine-User column by using a delimiter.

B. In a DAX function, create two calculated columns named Machine and User by using the substitute function.

C. From Power Query Editor, create a column that contains the last three digits of the Machine-User column.

D. in a DAX function, create two measures named Machine and User by using the substitute function.

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To create a clustered bar chart with "Machine" as the Axis and "DownloadMB" as the Values, you should choose:

A. From Power Query Editor, split the Machine-User column by using a delimiter.

This approach allows you to separate the "Machine-User" column into two distinct columns: "Machine" and "User." You can then use the "Machine" column as the Axis in your clustered bar chart and "DownloadMB" as the Values.

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