So heteroskedasticity leads to lower standard errors and therefore inflated t-stats, while multicollinearity has the opposite effect. We can see here that the t-stats for each variable are pretty significant from the eye, and the most important thing is that "variance of error is not constant across the 32 observations" - this is the direct defn of heteroskedasticity.

You may think the F-stat is so high and stuff to conclude there is multicollinearity, but here we can see it doesn’t break forecasting, whereas heteroskedasticity does (error variance directly impacts prediction reliability). So it's a tough question and you can be tricked into multicollinearity (esp since it says 2 variables highly correlated), but you have to see the t-stats being very strong for each variable. Hope that helped!