Can xgboost (or any other algorithm) give bad results with some bad features?
Yes it can for sure, some algorithms are more robust to this than others but doing proper feature selection is adviced. This is due to the curse of dimensionality. It's not only the algorithm but also has a lot to do with the amount of data points you have compared to the number of features. If you have 10,000,000 data points 150 features is not an issue, if you have 400 data points 150 features is way too much. How can you learn something when there is so little to learn from? Overfitting is a real issue for almost any algorithm, and noisy features make it more easy to learn things that are not actually there.