Why does central limit theorem give such big x in $\phi(x)$ I am solving a problem : The number of  new customers in the mall each day follows Poisson distirbution with $\lambda = 50$.  Find approximately the probability that after one year (200 working days) the number of customers that visited was between 950 and 1100.
My idea : Day i we have $X_i \sim P_o(\lambda) $ new customers . Now we need $P(950 \leq \sum_{i=1}^{200} X_i\leq 1100 )$.
$X_i$'s are independent and equally distirbuted. We have $200 > 30$ samples and therefore Central Limit Theorem should hold:
$$ Z=\frac{S_{200}-200\lambda}{\sqrt{200\lambda} } \sim N(0,1)$$
Then :
$P(950 \leq \sum_{i=1}^{200} X_i\leq 1100 ) = P(\frac{950-200*50}{\sqrt{200*50} } \leq Z \leq \frac{1100-200*50}{\sqrt{200*50} })$
And I could actually apply continuity correction and have instead:
$$ P(\frac{949.5-200*50}{\sqrt{200*50} } \leq Z \leq \frac{1100.5-200*50}{\sqrt{200*50} }=P(-90.5 \leq Z \leq -88.9)=\Phi(-88.9) - \Phi(-90.5) = 1-\Phi(88.9) - 1 + \Phi(90.5)= \Phi(90.5)-\Phi(88.9)$$
Why are they so huge  ?  At least in theory , CLT should hold , the conditions were satisfied.
Side note : We have at average 50 people per day and also 50 deviation . So let's suppose we have 50 and 0 people/per day alternatively --> after 200 days we should have 100* 50 = 5000 so less than 1100 seems small (if this kind of thought makes sense)

You're looking at a case where (assuming independence, though it would seem to be a somewhat questionable assumption), the distribution of the number of visitors in $200$ days has $\mu=10000$ and $\sigma=100$. That means that the values we're looking at are in the ballpark of 90 standard deviations below the mean.
The question asked about an event that was in that vicinity so fact that the z-values are around $-90$ is simply because that's the event the question chose to ask about.
This is just a straight calculation based on the values in the question; how many standard deviations from the mean we are looking of itself has nothing to do with the CLT (whether it 'holds' or not).
However, one thing to keep clearly in mind is that the approach to normality in an average or sum may have very poor relative accuracy in the extreme tail of the cdf on the left or the survivor function on the right (i.e. tail areas may be very inaccurate in the far tail), even in cases where relative error in the central area of the cdf (within a few standard deviations from the mean) is quite good.
I would not expect the relative error in probabilities calculated this way to be remotely close in this situation (the absolute error will of course be very small because both the Poisson and the normal tail areas will be extremely small, for all that one might be many many orders of magnitude larger than the other).

My suspicion is that the person who wrote the question made a calculation error and thought that they were asking about values close to $\mu$ rather than values relatively near $z=-100$.

so how would you answer the question? about the approximation of this probability

If there was a specific need for anything better than "if the assumptions are reasonable, this probability will be vanishingly small" then I might consider trying to use the connection between the Poisson and the chi-squared, but even there, accurate calculations at 90ish sd's below the mean would be difficult. Other possibilities would be to use still other approximations (there's normal approximations for various transformations of the Poisson, or of the chi-squared, for example, from which we could attempt to get a rough tail area), that might do better than the usual normal approximation, but again, accurate answers will be quite difficult; and out that far tail approximations that are often quite useful may actually be worse.
I expect we could make an argument that the true probability will be smaller than the value from the normal approximation (I expect it will be much, much smaller), and so the normal approximation would be a kind of upper bound on that probability. We could then approximate that normal cdf via the approximation $\Phi(z) = \phi(z)/|z|$ which works in the far left tail.
This should at least suffice to make it clear that the value of the required probability must be extremely small.

It is possible to get R to give answers (on the log scale) this far down in the Poisson and the normal. These values are unlikely to be very accurate but they may perhaps give some sense of just how vastly different in magnitude the Poisson and the normal may become in the extreme tail:
> ppois(1100,10000,log=TRUE)
[1] -6476.302
> ppois(950,10000,log=TRUE)
[1] -6818.063
> pnorm(1100,10000,100,log=TRUE)
[1] -3965.908
> pnorm(950,10000,100,log=TRUE)
[1] -4100.549

Note that the Poisson values (log-cdf) are in the "vicinity" of $-6500$  and the normal approximation values are in the "vicinity" of $-4000$. We're looking at the normal areas being on the order of $\exp(2500)$ times larger than the thing they're approximating, which is of "similar" size  to $10^{1000}$. (Here anything like similar or ballpark or vicinity is entirely unsuited to conveying how inaccurate these approximations are, my apologies; hence the use of scare quotes. Coming up with good expressions to actually motivate this without going back to mathematical statements is difficult.)
