It's honestly difficult for me to say because there are so many different ways to train AI. It really depends more on what the trainers configure to be a data point. Volume of files vs size of a single file aren't as important as what the AI believes is a data point and how the data points are weighted.

Just as a simple example, a data point may be considered a row on a spreadsheet without regard for how that data was split up across files. So ten files with 5 rows each might have the same weight as one file with 50 rows. But there's also a penalty concept in some models, so the trainer can set it so that data that all comes from one file may be penalized. Or the opposite could be true if data coming from the same file is deemed to be more important in some way.

In terms of how AIs make their decisions, that can also vary. But generally speaking, if 1000 pieces of data are used that are all similar in some way and one of them is somewhat different from the others, it is less likely that that one-off data will be used. It's much more likely to have an effect If 100 of the 1000 pieces of data have that same information. There's always the possibility of using that 1/1000 data, it's just less likely to have a noticeable effect.

AIs build confidence in responses based on how much a concept is reinforced, so you'd have to know something about the training algorithm to be able to intentionally impact the results.