Nope, i don't think they should or need to be able to.

These exercises are useful for mathematical maturity which results in intuition needed to develop novel algorithms or low level optimizations.

Not needed to use existing train and deploy ML algorithms in general.

Good news -- if you're not interested in extending state-of-the-art and simply want to call APIs, you don't have to learn ML deeply.

Depends on your definition of "ML practitioner", "building" and "ML". Look at the section on optimization - some people have an extremely good grasp of this and it helps them mentally iterate through possible loss functions and possible ways to update parameters and what can go wrong.

Some people see studying as a chore and want to learn the minimum to get the job done. Others find it insightful and fun and enjoy doing problems and reading material.

Both approaches make contributions and can lead to success, but in different ways.

I am curious about the same thing. I worked as a ML engineer for several years and have a couple of degrees in the field. Skimming over the document, I recognized almost everything but I would not be able to recall many of these topics if asked without context, although at one time I might have been able to.

What are others' general level of recall for this stuff? Am I a charlatan who never was very good at math or is it just expected that you will forget these things in time if you're not using them regularly?