You have a portfolio, $P$, filled with many positions, which are specifically dependent upon various asset price movements, say $X,Y and Z$. These price movements are random variables, but they may contain some inherent correlations. Suppose $X$ and $Y$ are highly correlated but $Z$ has small correlation to both $X$ and $Y$.
Now by asking "what is the exposure of my portfolio to just the AUD curve" you are effectively asking the analogous question what is the exposure of $P$ to just $Z$.
This is a difficult question.
What you might do as a starting point is assume some fixed correlations and volatilities and then vary $Z$. If $Z$ increases by 1, $X$ and $Y$ are assumed to increase by 0.2, and 0.4 respectively due to their assumed volatilities. Now revalue your portfolio. This is your risk to $Z$, correlation adjusted for $X$ and $Y$.
Say you get a value, i.e. 1000 USD exposure, there might be a high confidence in that value (eg. if you portfolio were exposed solely to the price of $Z$ then it would be exact), but it may have inherent uncertainty (if you portfolio is exposed solely to the price of $X$ then it depends entirely on the assumption of the correlation between $X$ and $Z$, and $X$'s volatility). If the correlation is not realised your exposure might be larger, zero or the negative of what you predicted.
Therefore as well as measuring your exposure to $Z$ you have a measure of the uncertainty in your measurement, which depends upon the fragility of your assumptions.
Note that PCA is a statistical technique that bases itself on covariance/correlation also so contains the same problem.