To agree with most of the answers here, and offer a similar-in-spirit example from a connected field, dealing with (definite) integrals in mathematical statistics is all about re-arranging them to look like a "known" probability density function (PDF). In essence this is also "recognizing a derivative" (better, "recognizing that we can arrive at a derivative") since the PDF is the derivative of the distribution function which is an integral. Sometimes we have also to transform the interval of integration in order to match it with the "benchmark" interval for which the distribution is known... at which point the integral "magically disappears from view" since it just became equal to the cumulative distribution function integrated over the support, and this always equals unity... leaving you with all the stuff you had to attach to it during the transformations -that admittedly may still include an integral, but of those that we nowadays consider "closed-form" since they do not have an analytical solution but are totally mapped numerically like the cumulative distribution function of the standard normal distribution.
Sometimes we arrive not at an integrand that represents a PDF (i.e. the derivative), and so at the cumulative distribution function, but at the moment generating function, which is also a known associated function with the distribution.
To provide the simplest example, if we are given the integral
$$I =\int_{-\infty}^{\infty} \exp\{-0.5x^2-ax\}dx$$
what we will do in mathematical statistics is
"Ah, exponential with the variable squared" $\rightarrow$ normal distribution: add and subtract the normalizing factor, and separate the two terms
$$I = \sqrt{2\pi}\int_{-\infty}^{\infty}e^{-ax} \left(\frac {1}{\sqrt {2\pi}}\exp\{-0.5x^2\}\right)dx$$
The term in the parenthesis is the probbaility density function of the standard normal distribution , typically denoted $\phi()$, so we have
$$I = \sqrt{2\pi}\int_{-\infty}^{\infty}e^{-ax} \phi(x)dx$$
The integral is now the moment generating function of the standard normal (the support is the "benchmark" one), $MGF(s) = E(e^{sx})$ evaluated at $s=-a$ so
$$I = \sqrt{2\pi}\cdot \exp\{0.5a^2\}$$
It is crucial to develop the skill to recognize the "kernels" of the probability density functions (the terms that involve the variable) as well as other exhaustively worked out entities in the field like the moment generating function, or the cumulant generating function. And so, earning marks for it is only natural.
For a more complicated example, see this post over at Cross Validated, where variable transformation is required apart from accounting for normalizing factors (especially under eq. $(3)$ there).