Manipulating the difficulty of guessing a correlation
You will probably need to do pilot testing to ascertain the difficulty of a given correlational task. 

From pilot testing, you would need to quantify the difficulty of the task. One option would be the mean discrepancy between actual and predicted correlation. However, there would be other metrics of both degree of error and difficulty.

I would hypothesise that the easiest correlations to guess based on scatterplots would be strong linear relationships involving bivariate normal distributions and lots of data (e.g., n > 1000).

There are a whole bunch of things that you could try to make the task more difficult. Here's what comes to my mind:


Outliers (both outliers that increase the correlation and outliers that decrease the correlations); vary the number of outliers in a particular region; combine outliers that increase and decrease the correlation; make outliers even more extreme
Non-linear relationships (e.g., mixtures of linear and another function such as quadratic, cyclical, stepwise; power functions, logistic fucntions, circular, etc.)
bimodal distributions on one or both variables
Highly skewed distributions on one or both variables
Fewer data points
data that yield correlations only a little bit above or below zero
where there is a function like a quadratic, make the x depend on y rather than y depend on x. 


More generally, I think that practice and feedback effects will also be relevant. I.e., it may be relevant to ascertain whether the differences in difficulty with novices also correspond to relative differences in difficulty with participants who have been exposed to the full range of items you have generated.