Answer:

Microsoft Store installation is typically faster for updates because:

• Automatic updates are enabled by default
• Updates happen in the background
• No need to manually download and reinstall
• Always stays current with latest features

Traditional .exe installation requires manual checking and downloading of updates.

Answer:

Data View is the correct choice because:

• Displays data in a tabular grid format
• Allows you to scroll through all rows and columns
• Easy to spot blank or null values
• Can sort and filter columns for inspection

Solution Steps:

1. Open Power BI Desktop
2. Click "Get Data" from Home ribbon
3. Select "Text/CSV" from the data source list
4. Browse and select "sales_data.csv"
5. Preview appears - Check if data looks correct
6. Click "Transform Data" to open Power Query Editor
7. Check Date column:
   • Click on Date column header
   • If showing "ABC" icon (text), change to Date type
   • Go to Transform tab → Data Type → Date
8. Verify other columns:
   • Product: Text type
   • Quantity: Whole Number
   • Price: Decimal Number
9. Click "Close & Apply" to load data

Date,Product,Quantity,Price
2024-01-15,Laptop,5,899.99
2024-01-16,Mouse,20,29.99
2024-01-17,Keyboard,15,79.99

Solution:

Method 1: Using Model View (Recommended)

1. Click the "Model View" icon on the left sidebar
2. You'll see both tables displayed as boxes
3. Drag "CustomerID" from Orders table
4. Drop it onto "CustomerID" in Customers table
5. Relationship is automatically created

Method 2: Using Manage Relationships

1. Go to Home ribbon → Click "Manage Relationships"
2. Click "New" button
3. Select "Orders" table and "CustomerID" column
4. Select "Customers" table and "CustomerID" column
5. Verify Cardinality: Many-to-One (*:1)
6. Set Cross filter direction: Single
7. Click OK

Why This Works: Each customer can have multiple orders (many-to-one relationship). This allows you to analyze order data by customer attributes like Region.

Detailed Solution:

Step 1: Load Data and Open Power Query

1. Get Data → Excel → Select file → Transform Data
2. You'll see the data in Power Query Editor

Step 2: Unpivot Columns (Critical Step)

1. Select the "Product" column (this is your identifier)
2. Right-click on Product column
3. Select "Unpivot Other Columns"
4. This transforms month columns into rows

Result After Unpivot:

Step 3: Rename Columns

1. Double-click "Attribute" column header
2. Rename to "Month"
3. Double-click "Value" column header
4. Rename to "Sales"

Step 4: Change Data Types

1. Click "Sales" column → Transform tab → Data Type → Whole Number
2. For Month column: Keep as text or convert to date if needed

Step 5: Close and Apply

1. Click "Close & Apply" in Home tab
2. Data is now in proper format for analysis

Product    | Month      | Sales
-----------+------------+-------
Laptop     | Jan 2024   | 5000
Laptop     | Feb 2024   | 6000
Laptop     | Mar 2024   | 5500
Mouse      | Jan 2024   | 1500
Mouse      | Feb 2024   | 1800
Mouse      | Mar 2024   | 1600

Why This Transformation?

• Wide format (months as columns) is hard to analyze
• Long format (rows per month) enables time-series analysis
• Can now create line charts showing trends over time
• Easy to filter by month or product
• Proper structure for relational data modeling

Power Query M Code (Advanced):

let
    Source = Excel.Workbook(File.Contents("path/to/file.xlsx")),
    Sheet = Source{[Name="Sheet1"]}[Data],
    PromotedHeaders = Table.PromoteHeaders(Sheet),
    UnpivotedColumns = Table.UnpivotOtherColumns(
        PromotedHeaders, 
        {"Product"}, 
        "Month", 
        "Sales"
    ),
    ChangedType = Table.TransformColumnTypes(
        UnpivotedColumns,
        {{"Sales", Int64.Type}}
    )
in
    ChangedType