Am I supposed to understand my training dataset for a machine learning project?
From the point of pure exercise in machine learning, you do not care whether the data is reliable and whether it was collected or taken from the ceiling. Your task is just to design a recognition algorithm that classifies the objects into groups based on the numbers associated with them. If you can do it, you pass, if not, you fail.

From the perspective of practical recognition of rocks, your algorithm (if it works) will be useful only if you trained it on accurate data or data with not too much noise and if you know what the numbers mean, so if you later are given the information in different units or on an altogether different scale (say, logarithmic instead of linear), you'll know how to convert before feeding them into the machine. If your algorithm is intended for real use, the people who requested the work should be most interested in getting things right, so you should be able to ask them as many relevant questions as you want. If it is just a practice problem, just forget about "rocks", "radiance", etc. View all that as an abstract classification problem about objects and numbers without any meaning whatsoever.

From what you are saying, it looks like you are in the first case scenario. I agree that the exercise is about as meaningful as the exercise in computing the area of the triangle with sides 6,7,8 and the altitude of length 5 drawn to the side of length 7. My daughter really had that as a problem on one of her exams and her teacher replied to my objections that at that stage of learning he just wanted to make sure that kids knew which side to multiply by the height. He wasn't a bad math. teacher overall, by the way, just a bit reckless about the details that weren't directly related to the current topic. Apparently, your professors have a similar attitude.