Bayesian and frequentist approaches give different answers in every problem since the interpretation of the two paradigms is different. In point estimation, Bayesians provide maximum a posterior (MAP) estimates and frequentist provides something else, e.g. maximum likelihood estimates (MLE) or method of moments. In interval estimation, Bayesians provide credible intervals (CrIs) while frequentists provide confidence intervals (CIs). In hypothesis testing, Bayesians provide posterior probabilities (or Bayes' factors) while frequentists provide pvalues. In each of these areas of statistic, Bayesians and frequentist differ in the answer they give.
Now, there are some times when the numerical results coincide, e.g. MAP=MLE and CrI=CI. Generally, this will only be true when certain improper prior distributions are used. There are also scenarios where hypothesis testing results in the same decision under a Bayesian and frequentist paradigm, but there are definitely times when they don't, e.g. Jeffrey-Lindley Paradox.