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Power BIbi_tool~10 mins

Power BI workflow (get data, transform, visualize, share) - Interactive Code Practice

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Practice - 5 Tasks
Answer the questions below
1fill in blank
easy

Complete the code to load data from a CSV file in Power BI.

Power BI
let Source = Csv.Document(File.Contents([1]),[Delimiter=",", Columns=5, Encoding=1252, QuoteStyle=QuoteStyle.None]) in Source
Drag options to blanks, or click blank then click option'
A"data.csv"
Bdata.csv
CFile.Load("data.csv")
DLoad("data.csv")
Attempts:
3 left
💡 Hint
Common Mistakes
Not using quotes around the file name.
Using incorrect functions to load the file.
2fill in blank
medium

Complete the code to rename a column in Power Query.

Power BI
Table.RenameColumns(Source, [1])
Drag options to blanks, or click blank then click option'
A{"OldName", "NewName"}
B["OldName" = "NewName"]
C{{"OldName", "NewName"}}
D[{"OldName", "NewName"}]
Attempts:
3 left
💡 Hint
Common Mistakes
Using curly braces incorrectly.
Using square brackets instead of curly braces.
3fill in blank
hard

Fix the error in the DAX measure to calculate total sales.

Power BI
Total Sales = SUM([1][SalesAmount])
Drag options to blanks, or click blank then click option'
AOrder
BOrders
CSales
DSalesData
Attempts:
3 left
💡 Hint
Common Mistakes
Using incorrect or partial table names.
Misspelling the table name.
4fill in blank
hard

Fill the blank to filter sales data for the year 2023 in DAX.

Power BI
Filtered Sales = CALCULATE(SUM(Sales[Amount]), Sales[Year] [1] 2023)
Drag options to blanks, or click blank then click option'
A=
B>
C<
D==
Attempts:
3 left
💡 Hint
Common Mistakes
Using '==' which is not valid in DAX.
Using greater or less than operators instead of equals.
5fill in blank
hard

Fill all three blanks to create a measure that calculates average sales per customer in DAX.

Power BI
Avg Sales per Customer = DIVIDE(SUM([1][SalesAmount]), DISTINCTCOUNT([2][[3]]))
Drag options to blanks, or click blank then click option'
ASales
BCustomers
CCustomerID
DOrders
Attempts:
3 left
💡 Hint
Common Mistakes
Mixing table names for sales and customers.
Using wrong column names for customer ID.