Multiple linear regression, I seek kind help for a result interpretation :)

So, I've been struggling with a statistics problem for some time now and was wondering if you could shed some light? The regression's Dependent variable is GPA and the two Independent variables are Gender and Examination.

The thing I can't understand is that if I do the regressions separately (also they don't correlate):

Gender on GPA, (F (1, 112) = 3.77, p = .054) and Beta (B = .03, p = .061), which is not significant.

Examination on GPA (F (1, 99) = 1.273, p = .262), Beta (B = .-063, p = .262), which is also not significant.

Now, when I combine those factors I get:
Gender and Examination on GPA: (F (2, 93) = 3.1, p = .05). So, it's just significant.
Gender, Beta (B = .037, p = .041), which now becomes significant as a predictor. But Examination, Beta (B = .-078, p = .146), still not significant. Also, the multicollinearity is fine, VIF's are below 2.
My question is, separately they are not significant, put when they are put together, the model becomes significant, Gender also becomes significant, yet Examination is not. Now, how should I interpret that? That only Gender has an effect on the GPA. But then, why didn't it have on its own? What about the Examination? What exactly is going on here?

So guys has somebody encountered similar situation? You have my gratitude :)