Conditional probability is denoted as P(A|B)=Probability of occurrence of A when B occurs OR Probability of Event A when B becomes a sample space.
Let us take an example:Let there be 5 white and 4 red balls.Two balls are drawn from the bag one after another without replacement. If we consider these events:
A= Drawing a white ball in first draw
B= Drawing a red ball in second draw.
Now,
P(B|A)= Probability of drawing a red ball from a bag containing 4 white and 4 red balls.
Here I fail to understand how event A can be considered the sample space for the given conditional probability.The sample space for Event A is just white balls while that of this probability is : total number of balls-one white ball from event B.Also what is the intuition behind considering Event A as a sample space if it is?
Adapting to your example let's give the balls numbers $1,2,\dots,9$ where the balls $1,2,3,4,5$ are white and the balls $6,7,8,9$ are red. Then by the experiment of drawing $2$ balls without replacement we can take $\Omega=\{\langle i,j\rangle\mid i,j\in\{1,2,\dots,9\}\wedge i\neq j\}$ to be the sample space.
The distinct outcomes are equiprobable.
For any event $E$ we have $P(E)=\frac{|E|}{|\Omega|}$.
Especially note that the denominator equals the cardinality of $|\Omega|$.
Event $A$ can be recognized as $\{\langle i,j\rangle\in\Omega\mid i\in\{1,2,3,4,5\}\}$ and event $B$ as $\{\langle i,j\rangle\in\Omega\mid j\in\{6,7,8,9\}\}$
Conditional probability $P(B\mid A)$ equals $\frac{P(A\cap B)}{P(B)}=\frac{|B\cap A|/|\Omega|}{|A|/|\Omega|}=\frac{|B\cap A|}{|A|}$.
Now note that here the denominator of the last expression equals the cardinality of $|A|$.
In some sense it has taken over the role of $\Omega$.
This because by computation of $P(B\mid A)$ we only look at outcomes that are in event $A$.