There is no one answer to this question.
To begin to understand why, you must first understand that the word "outlier" can refer to two different things:
an "outlier" in the dimension of the response. Typically this is measured by a studentized residual. If the absolute value of the studentized residual is over two or three, then you might say it's an outlier in $Y$-space.
an outlier in $x$-space, otherwise known as leverage. I think it's safe to say you don't have any here. If you did have a point that was an outlier in x-space (a high-leverage point), then you would have a dot way to the right or left of the rest of your points, ignoring how high or low it was on the y-axis.
However, you could have some leverage, depending on how you transform your predictor into more predictors, enlarging $X$-space. To measure leverage of an observation in this case, you can use diagonals of the hat matrix (otherwise known as the projection matrix.) They are always bounded between $0$ and $1$, but there are many rules-of-thumb for what merits are closer look.
It's hard to say if you have an outlier in $y$-space. This is because the value of the residual will be highly model-specific. For instance, I could come up with some transformations of your independent variable here that will give you a perfect fit, and all of your residuals will be $0$, and so therefore you will have no outliers in $y$-space either.
I wouldn't say the question is lousy. It might be, or it might not be. Interview questions are designed to elicit responses from the interviewee; they are not necessarily designed to be answerable.