100% Accuracy on test dataset using a previous developed model oputput
Welcome to the community. If I understood correctly:

you have is a dataset whose target labels (aka ground truth) you don't know, so you figured these out by assigning the output of another model from someone else --&gt; I guess this pre-trained model was built with the same dataset right?
you built a new model on this dataset, using as target values the other model's output as your ground truth, giving you a 100% accuracy --&gt; you convert the predicted probability to the corresponding label is it?

This might mean:

your model is providing the same output labels as the other model trained on the same dataset; this is not strange, mainly if the models are of the same type (e.g. tree-based models as your xgboost), mainly assuming the pretrained model outputs fit the real target values
the data is quite easy to &quot;learn&quot;, so both models provide similar results over the same target labeling
to take into account: accuracy might not be an optimal metric, taking a look at how much unbalanced is the dataset (it is, what is the ratio between the different classes).

Nevertheless, I would make sure to find the real and precise corresponding targets to go on with confidence on your problem.