Imagine you are working with a data cube for a retail business. The cube contains data about sales, and it has the following dimensions:

Time: $($coarsest$)$ Year, Quarter, Month, Day $($finest$)$

Product: $($coarsest$)$ Category, Subcategory, Product $($finest$)$

Location: $($coarsest$)$ Country, State, City $($finest$)$

The measures in the cube include:

Sales Amount

Quantity Sold

Here are some example data points in the OLAP cube:

$\backslash$table$[[$Year Q$,$Quarter,Month,Day,Category,Subcategory,Product,Country,State,City,Sales Amount,Quantity Sold$],[2024,$Q$1,$an$,15,$Electronics,Laptops,Laptop A$,$USA,California,Los Angeles,$1200,2],[2024,$Q$1,$an$,20,$Electronics,Smartphones,Phone B$,$USA,New York,New York,$800,1],[2024,$Q$2,$Apr,$10,$Accessories,Headphones,Headphone C$,$USA,Texas,Houston,$150,3]]$

Question:

Given the cube and the example data points, which of the following statements correctly describe roll$-$up$,$ drill$-$down, and slicing operations? $($One or more choices is correct$)$

a$.$ Drill$-$down from Subcategory to Category provides a more detailed view by breaking down Subcategory data into categories.

b$.$ Slicing the cube to show only data for January filters the data for the entire year including January.

c$.$ Roll$-$up from Month to Quarter aggregates data from months into quarters.

d$.$ Drill$-$down from Category to subcategory provides a more detailed view by breaking down category data into subcategories.

e$.$ Roll$-$up from Quarter to Month provides a more detailed view from quarters into months.

f$.$ Slicing the cube to show only data for January filters the data to include only sales in January.