In Bishop's textbook, is the example of overfitting exaggerated? 
Here, the data $x$ are randomly generated, and $t$ are generated by running $x$ through a function $\sin(2\pi x)$, then Gaussian noise is added.
Bishop's text then tries to fit those data using a polynomial of 9th degree, i.e., $y(x,w) = w_o + w_1 x + \ldots + w_Mx^M$ (Shown in figure)
Then they showed that this results in large mean squared error for test data.
And the reason being that the weights associated with each of the parameters is extremely large, something like $w_i = O(1000000)$
But is this a fluke?
Why does the polynomial's weight need to be so large? 
A polynomial of 9th order needs to have 8 bumps.
That's all it needs. So Bishop's curve could simply look like the black curve which I drawn.

No more huge weights, problem solved.
Can someone comment on why must the weights associated with the polynomial be so large when I fit a high order polynomial, instead of having smaller weight like the curve I drew? 
Is this simply a simulation error in Bishop's text, or does solving the optimization problem actually give you weight that large (which I do not believe)?
Added: Exact weights as shown in textbook

Experiment: Poly-fitting in R using same data

 A: 
A polynomial of 9th order needs to have 8 bumps.
That's all it needs. So Bishop's curve could simply look like the black curve which I drawn.

When we eyeball a curve, we tend to draw something more like a spline: a piecewise-polynomial solution to an optimization problem that penalizes jagged curves in favor of smoother curves.
Indeed, with the help of @mark-white's example data, we can ask R's smooth.spline() function to fit a cubic smoothing spline. By using as many degrees of freedom as there are data points, the spline will interpolate, and we get a curve very much like your hand-drawn black curve.
dat <- data.frame(
  x = seq(0, 1, length.out = 10), 
  y = c(.3, .8, 1, .95, .1, .15, -.8, -.5, -.6, .25)
)
plot(dat$x, dat$y)
ss = smooth.spline(dat$x, dat$y, df = nrow(dat))
lines(predict(ss, x = seq(0, 1, length = 100)))



Can someone comment on why must the weights associated with the polynomial be so large when I fit a high order polynomial, instead of having smaller weight like the curve I drew?
Is this simply a simulation error in Bishop's text, or does solving the optimization problem actually give you weight that large (which I do not believe)?

As the other answers have shown, the extreme weights and jagged nature of Bishop's polynomial are correct for this dataset. What you've drawn isn't a bad fit to the data, but it also isn't a single polynomial. As I understand it, the smoothing spline is drawing separate cubic curves between each pair of data points, and ensuring that the overall curve looks smooth by matching up derivatives where the cubics meet. Since Bishop's single polynomial doesn't have this piecewise nature, it doesn't look as smooth as the spline or your sketch. Although the regularized spline estimate still overfits compared to the true sine curve, it isn't as bad as the polynomial.
In other words: Bishop's example isn't an exaggeration, and overfitting is a real problem, but we do have some ways to mitigate it.
