Comprehensive Detailed Explanation
Requirement
We have two columns in Power Query Editor:
Order_Date
Shipping_Date
We need to calculate the number of days between the two dates.
Step 1: How date differences work in Power Query (M language)
Subtracting two date or datetime values in Power Query returns a duration value.
Example:
[Shipping_Date] - [Order_Date]
This produces a duration such as 3.00:00:00 (3 days).
Step 2: Converting duration into days
To get the number of days as an integer, we use Duration.Days().
Example:
Duration.Days([Shipping_Date] - [Order_Date])
This returns 3 if the shipping date is 3 days after the order date.
Step 3: Evaluate other options
A). Duration.From # Converts a value to a duration type but does not extract the number of days.
B). Date.AddDays # Adds days to a given date, not used for calculating the difference.
C). Duration.Days # Correct, extracts total days from a duration.
D). DateTime.LocalNow # Returns the current system datetime, unrelated to this requirement.
References
Duration.Days function (Power Query M)
Working with dates in Power Query

Explanation:
The results will form a hierarchy of folders for each partition key. - Yes The resulting file partitions can be read in parallel across multiple nodes. - Yes The resulting file partitions will use file compression. - No Partitioning data by columns such as year, month, and day, as shown in the DataFrame write operation, organizes the output into a directory hierarchy that reflects the partitioning structure. This organization can improve the performance of read operations, as queries that filter by the partitioned columns can scan only the relevant directories. Moreover, partitioning facilitates parallelism because each partition can be processed independently across different nodes in a distributed system like Spark. However, the code snippet provided does not explicitly specify that file compression should be used, so we cannot assume that the output will be compressed without additional context.
References =
DataFrame write partitionBy
Apache Spark optimization with partitioning

To view column information like the one shown in the exhibit in Power Query, you need to select the options that enable profiling and display quality and distribution details. These are: A. Enable column profile - This option turns on profiling for each column, showing statistics such as distinct and unique values. B. Show column quality details - It displays the column quality bar on top of each column showing the percentage of valid, error, and empty values. E. Show column value distribution - It enables the histogram display of value distribution for each column, which visualizes how often each value occurs.
References: These features and their descriptions are typically found in the Power Query documentation, under the section for data profiling and quality features.