We revisit the problem of differentially private release of classification queries. In this problem, the goal is to design an algorithm that can accurately answer a sequence of classification queries based on a private training set while ensuring differential privacy. We formally study this problem in the agnostic PAC model and derive a new upper bound on the private sample complexity. Our results improve over those obtained in a recent work [BTT18] for the agnostic PAC setting. In particular, we give an improved construction that yields a tighter upper bound on the sample complexity. Moreover, unlike [BTT18], our accuracy guarantee does not involve any blow-up in the approximation error associated with the given hypothesis class. Given any hypothesis class with VC-dimension $d$, we show that our construction can privately answer up to $m$ classification queries with average excess error $α$ using a private sample of size $\approx \frac{d}{α^2}\,\max\left(1, \sqrt{m}\,α^{3/2}\right)$. Using recent results on private learning with auxiliary public data, we extend our construction to show that one can privately answer any number of classification queries with average excess error $α$ using a private sample of size $\approx \frac{d}{α^2}\,\max\left(1, \sqrt{d}\,α\right)$. When $α=O\left(\frac{1}{\sqrt{d}}\right)$, our private sample complexity bound is essentially optimal.