The correct answer is False. Cumulative distribution functions (CDFs) are used to specify the distribution of univariate random variables. A CDF is a function that gives the probability that a random variable will be less than or equal to a certain value. For example, if the CDF of a random variable $X$ is $F_X(x)$, then $F_X(x) = \Pr(X \leq x)$.
Multivariate random variables are random variables that take on values in multiple dimensions. The distribution of a multivariate random variable is specified by its joint cumulative distribution function (CDF). The joint CDF of a multivariate random variable $(X_1, X_2, \ldots, X_n)$ is a function that gives the probability that all of the random variables will be less than or equal to certain values. For example, if the joint CDF of a multivariate random variable $(X_1, X_2)$ is $F_{X_1, X_2}(x_1, x_2)$, then $F_{X_1, X_2}(x_1, x_2) = \Pr(X_1 \leq x_1, X_2 \leq x_2)$.
In conclusion, CDFs are used to specify the distribution of univariate random variables, while joint CDFs are used to specify the distribution of multivariate random variables.