Decidability of decision problems
A good rule of thumb is to use Church's thesis: can you personally solve the problem? While this is of course informal, in practice it works very well after you develop a bit of experience with Turing machines. (That said, ultimately the theorem you'll learn and use here is Rice's theorem.)

By way of example, let's first consider the following two sets:


Those Turing machines which halt (on input $0$) in at most $42$ steps.
Those Turing machines which halt (on input $0$) and output $42$.


It should be fairly easy to see that we can informally compute the former: given a Turing machine, just run it on input $0$ for $42$ steps and see what happens. The latter, though, is harder: if I'm presented with a candidate Turing machine, if I know it halts I can run it until it halts and check whether the output is $42$, but in the absence of that information I seem to be stuck since I don't see a way around the infinite search involved in "wait until it halts, if it ever does."



So now let's look at a couple candidates on your list.


(1): in principle, we'd need to check each possible input to be confident that a Turing machine has the desired property: a single counterexample tells is it doesn't, but we don't know where such a counterexample might be and so as we go along checking all possible strings we'll never be fully confident that there isn't a counterexample (= a string of odd length which is accepted) which we just haven't found yet.
(2): Here, there are only finitely many things we need to check, namely all those strings of length $&lt;2019$. However, think about what's involved in checking a single candidate: the only a priori way to tell that a Turing machine doesn't accept a string is to run it forever, and "at the end of forever" you'll know that it in fact doesn't accept that string, but that's clearly bonkers. So there's still an infinite search involved, and this appears undecidable.




Now how do we make further distinctions between the undecidable sets - in particular, between semidecidable (more commonly, computably enumerable) and not-even-semidecidable?

Again, Church's thesis is the key: is it the case that, in order to be certain that something is in our set, we need to see some finite amount of information? If so, then it's computably enumerable. Otherwise, it's not - and in particular, if it's falsified by a counterexample (instead of verified by a witness) its complement is computably enumerable. So our Church's thesis analysis of (1) and (2) says that each is not even computably enumerable.