**Probability Mass Function (PMF)**

The probability mass function (PMF) is a function that describes the probability distribution of a discrete random variable. It gives the probability of each possible outcome.

**Sum of PMF**

The sum of the probability mass function is equal to 1. This means that when you sum up the probabilities of all possible outcomes of a discrete random variable, the total probability will always equal 1.

**Explanation**

To understand why the sum of the PMF is equal to 1, let's consider a simple example. Suppose we have a fair six-sided die. The possible outcomes are the numbers 1, 2, 3, 4, 5, and 6.

The probability of each outcome is 1/6 because the die is fair and each side has an equal chance of landing face up.

Let's calculate the sum of the PMF:

PMF(1) = 1/6
PMF(2) = 1/6
PMF(3) = 1/6
PMF(4) = 1/6
PMF(5) = 1/6
PMF(6) = 1/6

Sum of PMF = 1/6 + 1/6 + 1/6 + 1/6 + 1/6 + 1/6 = 6/6 = 1

As you can see, the sum of the PMF is equal to 1. This is because the sum of all possible outcomes' probabilities should always equal 1, representing the total probability of the entire sample space.

This property holds true for any discrete random variable. The probabilities assigned to each possible outcome must add up to 1.

Therefore, the correct answer is option 'C': 1.