It means that you have at least one observation that may be problematic. This could be a problem in your data, like an input mistake (you wrote 200, instead of 2), or more generally the model is not able to actually model the observation(s). For example you are modeling count data using a Poisson distribution, but your data is overdispersed so a NegativeBinomial will be probably a better idea. To help diagnose the problem you can use LOO and functions like arviz.plot_khat — ArviZ dev documentation and arviz.plot_elpd — ArviZ dev documentation
If you are seeing those warnings, it means that the approximations used to compute WAIC and LOO may not be reliable. So it is better to solve those problems. Alternative you can use az.LOO to get the ELPD of the non-problematic observations (k hat <0.7) and then explicitly compute the ELPD for the problematic observations (k hat > 0.7) by refitting the model and actually leaving one observation out. Of course this is only a good idea if you just have a few points, otherwise the cost of refitting the model many times will be too expensive. You can read more about this here Articles • loo