Depends on your use case, once again. If your use case requires that arithmetic involving cents is exact, then yes defining 1.0d = 1 cent guarantees that, just as using the int value 1 = 1 cent guarantees that. Floating point numbers can represent and perform exact arithmetic on integer values, no loss of precision.

The original example was being concerned that 10 cents plus 20 cents should equal 30 cents exactly, not 30.000000...2 cents. Well if you set 1.0d = 1 cent, you get that exact result. In fact for any arithmetic performed on values greater than or equal to 1 cent, you are guaranteed to get exact results just like you get exact results using an int to represent cents.

Now if your use case does not require exact arithmetic on cents, then by all means stick to using 1.0d = 1 dollar. Evaluate your use case and decide on an appropriate scale to use by default.

Where the benefit of floating point kicks in, for certain use cases, is that you can continue to use the same representation with operations that don't work well with fixed precision, like interest rate calculations, financial derivatives, or more advanced financial modelling techniques and you get these operations at significant orders of magnitude faster than you would using decimal representations.

And I'm not talking like 20% faster or 50% faster... the difference between using a binary floating point representation versus decimal ranges from 10x to 100x, and the difference between using a double versus a big decimal is on the order of 1000x.

In order words, a financial report that can be generated in 1 second using binary floats can end up taking almost 2 minutes using a decimal representation, and 16 minutes using a big decimal.