Explanation:

We are designing a star schema for sales transactions. In a star schema:
Dimension tables contain descriptive attributes (e.g., Customer, Product, Date).
Fact tables contain measurable, quantitative data (e.g., SalesTransactionID, Quantity, UnitPrice).
Factless fact tables capture events without numeric measures.
Junk dimensions store miscellaneous low-cardinality attributes.
The requirement says dimension tables will use Type 2 Slowly Changing Dimensions (SCD) # so Customer and Date attributes belong to Dimension tables.
Field-by-field classification
CustomerCode
A business key (natural key) for the customer dimension.
Belongs in the Customer Dimension table.
answer: Dimension.
CustomerName
A descriptive attribute of the customer.
Belongs in the Customer Dimension table.
answer: Dimension.
SaleDate
A time attribute used for analysis.
Belongs in the Date Dimension table.
answer: Dimension.
Final Answer:
CustomerCode # Dimension
CustomerName # Dimension
SaleDate # Dimension
References:
Star schema design for Power BI / Fabric
Slowly Changing Dimensions in data warehousing
# This follows Kimball's star schema best practices.

Explanation:

The Spark engine will read only the ' SalesOrderNumber ' , ' OrderDate ' , ' CustomerName ' , ' UnitPrice ' columns from Sales_raw.csv. - Yes Removing the partition will reduce the execution time of the query. - No Adding inferSchema= ' true ' to the options will increase the execution time of the query. - Yes The code specifies the selection of certain columns, which means only those columns will be read into the DataFrame. Partitions in Spark are a way to optimize the execution of queries by organizing the data into parts that can be processed in parallel. Removing the partition could potentially increase the execution time because Spark would no longer be able to process the data in parallel efficiently. The inferSchema option allows Spark to automatically detect the column data types, which can increase the execution time of the initial read operation because it requires Spark to read through the data to infer the schema.

The Table.Profile function in Power Query is used to generate column statistics such as count, average, null count, distinct count, and standard deviation. You can use this function as follows:
* Invoke the Power Query Editor.
* Apply the Table.Profile function to your table.
* The result will be a table where each row represents a column from the original table, and each column in the result represents a different statistic such as those listed in the requirement.
References: The use of Table.Profile is part of Power Query M function documentation where it explains how to gather column statistics for a given table.

Upon Success: downstream runs only if the first activity succeeds.
Upon Failure: downstream runs only if the first activity fails.
Upon Skip: downstream runs if the activity was skipped.
Upon Completion: downstream runs regardless of whether the first activity succeeded or failed.
Since we want the second activity to run even if the first fails, the correct answer is Upon Completion.
Reference: Pipeline activity dependencies in Fabric Data Factory

Explanation:
For the HighestSellingPrice, you should use the GREATEST function to find the highest value from the given price columns. However, T-SQL does not have a GREATEST function as found in some other SQL dialects, so you would typically use a CASE statement or an IIF statement with nested MAX functions. Since neither of those are provided in the options, you should select MAX as a placeholder to indicate the function that would be used to find the highest value if combining multiple MAX functions or a similar logic was available.
For the TradePrice, you should use the COALESCE function, which returns the first non-null value in a list.
The COALESCE function is the correct choice as it will return AgentPrice if it ' s not null; if AgentPrice is null, it will check WholesalePrice, and if that is also null, it will return ListPrice.
The complete code with the correct SQL functions would look like this:
SELECT ProductID,
MAX(ListPrice, WholesalePrice, AgentPrice) AS HighestSellingPrice, -- MAX is used as a placeholder COALESCE(AgentPrice, WholesalePrice, ListPrice) AS TradePrice FROM Sales.Products Select MAX for HighestSellingPrice and COALESCE for TradePrice in the answer area.