Classifying and filtering out noise from zero crossings
Without knowing the context, here is what probably is meant:

3.75 refers to a more real-world sensor. The sensor described in 3.74 apparently is assumed to be perfect, ie whenever there is a negative/positive sensor data, the true signal does have the associated property. Ideal sensors, however, do not exist and 3.75 tells you, that your sensor does have imperfections that lead to spurious false sign alterations. The sensor shall be assumed to produce randomly additional zero-crossings that might look like that:

Instead of: 1 0  1 2 3  1 -5 -2 -2 -1 5 3 
You get:    1 3 -2 3 2 -2 -4  3 -2 -1 2 3 


Your example data suggested that a positive/negative periods are typically longer than just one measurement. You are supposed to "harden" your filter a bit to still provide the valid zero-crossings. This filter will never be perfect, but it should be better than the naive filter you probably implemented for 3.74.

In 3.74 you probably implemented a simple if-check: If the sign alternates between measurement $n-1$ and $n$, spit out the corresponding 1 or -1. A hardened filter could be implemented by looking back $n-k$ points with $k=1,2,3,...$ chosen appropriately and spit out a 1 or -1 if all of the $k$ points were of the same sign. This, however, would introduce a delay into your filter, and it would miss zero-crossings that are shorter than $k$ points. So, key would be to know the shortest duration between two zero crossings and choose $k$ smaller than that, and it would be important to know whether or not the delay is acceptable.

And no, noise does not refer to the zeros in your signal ;)