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NEW QUESTION # 120
You have a Fabric warehouse that contains a table named SalesOrderDetail. SalesOrderDetail contains three columns named OrderQty, ProductID and SalesOrderlD. SalesOrderDetail contains one row per combination of SalesOrderlD and ProductID.
You need to calculate the proportion of the total quantity of each sales order represented by each product within the sales order.
Which T-SQL statement should you run?
Answer: D
Explanation:
Comprehensive Detailed Explanation
We need to calculate the proportion of the total quantity of each sales order represented by each product within the sales order.
Step 1: Analyze the requirement
Table: SalesOrderDetail
Columns: OrderQty, ProductID, SalesOrderID
Each row = one product in a sales order.
Requirement: For each SalesOrderID, calculate what percentage each product contributes to the total order quantity.
This means we must:
Calculate the total order quantity per SalesOrderID.
Divide each product's OrderQty by that total.
Express it as a percentage.
Step 2: Evaluate the options
A). Uses OVER(ORDER BY ProductID)
Wrong: ORDER BY does not segment data, it only defines sequence. We need grouping by sales order.
B). Uses OVER(PARTITION BY ProductID)
Wrong: This would calculate percentages per product across all sales orders, not per sales order.
C). Uses OVER(PARTITION BY SalesOrderID)
Correct: This computes the total OrderQty per SalesOrderID and divides each product's quantity by that total.
Exactly what the requirement asks.
D). Uses OVER(ORDER BY SalesOrderID)
Wrong: Again, ORDER BY just sequences rows, does not group them.
Step 3: Correct Query
SELECT
SalesOrderID,
ProductID,
OrderQty,
CAST(1. * OrderQty / SUM(OrderQty)
OVER(PARTITION BY SalesOrderID) * 100 AS DECIMAL(5,2))
AS PercentByProductID
FROM Sales.SalesOrderDetail;
Why Option C is Correct
PARTITION BY SalesOrderID ensures the denominator is the total quantity for the current order.
This produces the correct percentage breakdown of each product inside its sales order.
References
T-SQL Window Functions
Aggregate Functions with PARTITION BY
Microsoft Fabric Warehouse T-SQL Support
NEW QUESTION # 121
You have a Fabric warehouse that contains a table named Staging.Sales. Staging.Sales contains the following columns.
You need to write a T-SQL query that will return data for the year 2023 that displays ProductID and ProductName arxl has a summarized Amount that is higher than 10,000. Which query should you use?
Answer: A
Explanation:
The correct query to use in order to return data for the year 2023 that displays ProductID, ProductName, and has a summarized Amount greater than 10,000 is Option B. The reason is that it uses the GROUP BY clause to organize the data by ProductID and ProductName and then filters the result using the HAVING clause to only include groups where the sum of Amount is greater than 10,000. Additionally, the DATEPART(YEAR, SaleDate) = '2023' part of the HAVING clause ensures that only records from the year 2023 are included.
References = For more information, please visit the official documentation on T-SQL queries and the GROUP BY clause at T-SQL GROUP BY.
NEW QUESTION # 122
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a table named Nyctaxi_raw. Nyctaxi_raw contains the following columns.
You create a Fabric notebook and attach it to lakehouse1.
You need to use PySpark code to transform the data. The solution must meet the following requirements:
* Add a column named pickupDate that will contain only the date portion of pickupDateTime.
* Filter the DataFrame to include only rows where fareAmount is a positive number that is less than 100.
How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* Add the pickupDate column: .withColumn("pickupDate", df["pickupDateTime"].cast("date"))
* Filter the DataFrame: .filter("fareAmount > 0 AND fareAmount < 100")
In PySpark, you can add a new column to a DataFrame using the .withColumn method, where the first argument is the new column name and the second argument is the expression to generate the content of the new column. Here, we use the .cast("date") function to extract only the date part from a timestamp. To filter the DataFrame, you use the .filter method with a condition that selects rows where fareAmount is greater than 0 and less than 100, thus ensuring only positive values less than 100 are included.
NEW QUESTION # 123
You need to create a DAX measure to calculate the average overall satisfaction score.
How should you complete the DAX code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* The measure should use the AVERAGE function to calculate the average value.
* It should reference the Response Value column from the 'Survey' table.
* The 'Number of months' should be used to define the period for the average calculation.
To calculate the average overall satisfaction score using DAX, you would need to use the AVERAGE function on the response values related to satisfaction questions. The DATESINPERIOD function will help in calculating the rolling average over the last 12 months.
NEW QUESTION # 124
You have a Microsoft Power BI semantic model that contains a measure named TotalSalesAmount.
TotalSalesAmount returns a sales revenue amount that is translated into a selected currency.
You need to ensure that the value returned by TotalSalesAmount is formatted to use the correct currency symbol.
What should you include in the solution?
Answer: A
Explanation:
Scenario:
Semantic model contains a measure: TotalSalesAmount .
This measure returns sales revenue translated into a selected currency .
Requirement: Format the result using the correct currency symbol dynamically.
Analysis:
Dynamic format string in DAX is specifically designed for applying formats (like currency symbols) dynamically based on slicers or calculation context.
WINDOW function # used for windowing calculations, not formatting.
Linguistic schema # used for Q & A natural language, not for formatting.
Field parameter # used to swap fields/measures dynamically, not for formatting.
A). A dynamic format string
NEW QUESTION # 125
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